Updated on 2024/03/25

写真a

 
KOMAMIZU Takahiro
 
Organization
Mathematical and Data Science Center Associate professor
Graduate School
Graduate School of Informatics
Title
Associate professor
Contact information
メールアドレス

Degree 3

  1. 博士(工学) ( 2015.3   筑波大学 ) 

  2. 修士(工学) ( 2011.3   筑波大学 ) 

  3. 学士(情報工学) ( 2009.3   筑波大学 ) 

Research Interests 5

  1. 情報検索

  2. Linked Open Data

  3. OLAP

  4. データ工学

  5. データベース

Research Areas 3

  1. Informatics / Web informatics and service informatics

  2. Informatics / Database

  3. Informatics / Web informatics and service informatics

Research History 5

  1. Nagoya University   Mathematical and Data Science Center   Associate professor

    2022.3

  2. Nagoya University   Institute of Innovation for Future Society   Designated lecturer

    2021.4 - 2021.12

  3. Nagoya University   Information Technology Center   Assistant Professor

    2018.2 - 2021.3

  4. University of Tsukuba   Center for Computational Sciences   Researcher

    2015.4 - 2018.1

      More details

    Country:Japan

  5. University of Tsukuba   Center for Computational Sciences   Researcher

    2015.4 - 2018.1

Education 3

  1. University of Tsukuba

    2011.4 - 2015.3

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    Country: Japan

  2. University of Tsukuba

    2009.4 - 2011.3

      More details

    Country: Japan

  3. University of Tsukuba

    2005.4 - 2009.3

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    Country: Japan

Professional Memberships 8

  1. 電子情報通信学会   正会員

    2018.6

  2. 人工知能学会   正会員

    2018.4

  3. 言語処理学会   正会員

    2018.2

  4. the American Association for Artificial Intelligence

    2016.12 - 2017.12

  5. Association for Computing Machinery   Regular Member

    2012.5

  6. Institute of Electrical and Electronics Engineers

    2012.3

  7. 情報処理学会   正会員

    2010.6

  8. 日本データベース学会   正会員

    2008.12

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Committee Memberships 48

  1. 東海関西データベースワークショップ 2023   プログラム委員会 プログラム委員  

    2023.9   

  2. IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI 2023)   Workshop Co-Chair  

    2023.1 - 2023.8   

  3. 11th IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences (SCSN@ICSC 2023)   PC member  

    2022.9 - 2023.2   

  4. 東海関西データベースワークショップ 2022   プログラム委員会 プログラム委員  

    2022.9   

  5. 第15回データ工学と情報マネジメントに関するフォーラム (DEIM 2023)   実行委員会 ローカル共同委員長  

    2022.4 - 2023.3   

  6. 数理・データサイエンス・AI教育強化拠点コンソーシアム   調査研究分科会 委員  

    2022.4 - 2023.3   

  7. 1st Workshop on User-Centric Narrative Summarization of Long Videos (NarSUM@ACM MM 2022)   Web and SNS Chair  

    2022.4 - 2022.10   

  8. 日本データベース学会   システム委員会 構成メンバー  

    2022.3 - 2022.9   

  9. 10th IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences (SCSN@ICSC 2022)   PC member  

    2021.11 - 2022.1   

  10. 東海関西データベースワークショップ 2021   プログラム委員会.プログラム委員  

    2021.9   

  11. 11th International Symposium on Information and Communication Technology (SoICT 2022)   PC member  

    2021.7 - 2022.12   

  12. 第14回データ工学と情報マネジメントに関するフォーラム (DEIM 2022)   実行委員会 ローカル共同委員長  

    2021.4 - 2022.3   

  13. TMI Educational Video Competition in collaboration with IV21   Organizing Co-Chair  

    2021.4 - 2021.8   

  14. the 9th International Workshop on Semantic Computing for Social Networks (SCSN 2021)   Program committee  

    2020.11 - 2021.1   

  15. 9th International Workshop on Semantic Computing for Social Networks (SCSN@ICSC 2021)   PC member  

    2020.11 - 2021.1   

  16. 第18回情報学ワークショップ   プログラム委員  

    2020.9 - 2020.11   

  17. 第18回情報学ワークショップ (WiNF 2020)   プログラム委員会 プログラム委員  

    2020.9 - 2020.11   

  18. he 4th International Conference on Multimedia Information Processing and Retrieval (MIPR 2021)   Web and SNS Co-Chair  

    2020.6   

  19. 4th International Conference on Multimedia Information Processing and Retrieval (MIPR 2021)   Web and SNS Co-Chair  

    2020.6 - 2021.12   

  20. the r3rdInternational Workshop on EntitY REtrieval (EYRE@CIKM2020)   Program committee  

    2020.4 - 2020.10   

  21. 3rd International Workshop on EntitY REtrieval (EYRE@CIKM2020)   PC member  

    2020.4 - 2020.10   

  22.   PC member  

    2019.9 - 2020.2   

  23. 8th International Workshop on Semantic Computing for Social Networks (SCSN@ICSC 2020)   PC member  

    2019.9 - 2020.2   

  24. 第17回情報学ワークショップ プログラム委員会   プログラム委員  

    2019.7 - 2019.11   

  25. 第17回情報学ワークショップ (WiNF 2019)   プログラム委員会 プログラム委員  

    2019.7 - 2019.11   

  26. 電子情報通信学会データ工学研究専門委員会   専門委員  

    2019.6   

  27. 電子情報通信学会   データ工学研究専門委員会 専門委員  

    2019.6 - 2023.6   

  28.   PC member  

    2019.4 - 2019.11   

  29. 2nd International Workshop on EntitY REtrieval (EYRE@CIKM2019)   PC member  

    2019.4 - 2019.11   

  30. 第12回データ工学と情報マネジメントに関するフォーラム (DEIM 2020) 実行委員会   幹事(Web・出版担当)  

    2019.3   

  31. 第12回データ工学と情報マネジメントに関するフォーラム (DEIM 2020)   実行委員会 幹事(Web・出版担当)  

    2019.3 - 2020.5   

  32.   PC member  

    2018.12 - 2019.2   

  33. 7th International Workshop on Semantic Computing for Social Networks (SCSN@ICSC 2019)   PC member  

    2018.12 - 2019.2   

  34.   PC member  

    2018.10 - 2019.7   

  35. 第16回情報学ワークショップ 実行委員会   現地実行委員  

    2018.9 - 2918.11   

  36. 第11回Webとデータベースに関するフォーラム (WebDB Forum 2018)   学生奨励賞評価委員会 委員  

    2018.9   

  37. 第16回情報学ワークショップ (WiNF 2018)   実行委員会 委員(会計担当)  

    2018.7 - 2019.3   

  38. 言語処理学会第25回年次大会 実行委員会   実行委員  

    2018.6 - 2019.3   

  39. 言語処理学会第25回年次大会 (NLP 2019)   実行委員会 委員  

    2018.6 - 2019.3   

  40. 第11回 Webとデータベースに関するフォーラム 実行委員会   出版・印刷担当幹事  

    2018.5 - 2018.9   

  41. 第11回Webとデータベースに関するフォーラム (WebDB Forum 2018)   実行委員会 出版・印刷担当幹事  

    2018.5 - 2018.9   

  42. 第11回データ工学と情報マネジメントに関するフォーラム (DEIM 2019)   実行委員会 Web・出版委員長  

    2018.3 - 2019.5   

  43. 第11回データ工学と情報マネジメントに関するフォーラム (DEIM 2019) 実行委員会   Web・出版委員長  

    2018.3 - 2019.3   

  44. 第10回 Webとデータベースに関するフォーラム 実行委員会   Web担当幹事  

    2017.9   

  45. 第10回Webとデータベースに関するフォーラム (WebDB Forum 2017)   実行委員会 Web担当幹事  

    2017.8 - 2017.9   

  46. 第9回 Webとデータベースに関するフォーラム 学生奨励賞評価委員会   学生奨励賞評価委員  

    2016.9   

  47. DBSJ電子広報編集委員会   編集委員  

    2015.8   

  48. 日本データベース学会   電子広報編集委員会 編集委員  

    2015.7 - 2022.3   

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Awards 27

  1. Best Paper Award

    2023.8   DEXA 2023   Towards Ensemble-Based Imbalanced Text Classification Using Metric Learning

    Takahiro Komamizu

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    Award type:Award from international society, conference, symposium, etc.  Country:Malaysia

  2. 人工知能学会研究会優秀賞

    2021.6   第51回SWO研究会   法令沿革LOD構築のためのDBpediaにおける法令エンティティの同定

    駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  3. MIRU インタラクティブ発表賞

    2023.7   MIRU 2023   類音語の連想性を考慮した未知語の発音に対する画像生成

    松平 茅隼, カストナーマークアウ レル, 駒水 孝裕, 平山 高嗣, 道満 恵介, 川西 康友, 井手 一郎

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  4. 学生プレゼンテーション賞

    2023.3   DEIM 2023   固有表現タグおよびPOSタグによる交換制約付きデータ拡張手法

    寺本 優香, 駒水 孝裕, 波多野 賢治

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  5. Best Paper Runner-up

    2022.12   The 24th International Conference on Asia-Pacific Digital Libraries (ICADL 2022)   Towards Efficient Data Access Through Multiple Relationship in Graph-Structured Digital Archives

    Kazuma Kusu, Takahiro Komamizu, Kenji Hatano

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    Award type:Award from international society, conference, symposium, etc.  Country:Viet Nam

  6. Best Paper Runner-up

    2022.12   ICADL 2022   Towards Efficient Data Access Through Multiple Relationship in Graph-Structured Digital Archives

    Kazuma Kusu, Takahiro Komamizu, Kenji Hatano

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    Award type:Award from international society, conference, symposium, etc.  Country:Viet Nam

  7. 人工知能学会研究会優秀賞

    2021.6   人工知能学会   法令沿革LOD構築のためのDBpediaにおける法令エンティティの同定

    駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  8. 最優秀賞

    2020.11   第18回情報学ワークショップ   利用規約中の不公平文検出における不均衡データ分類に対する EasyEnsemble の利用

    近藤 匠, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc. 

  9. 最優秀賞

    2020.11   第18回情報学ワークショップ   利用規約中の不公平文検出における不均衡データ分類に対する EasyEnsemble の利用

    近藤 匠, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  10. オンラインプレゼンテーション賞

    2020.3   第12回データ工学と情報マネジメントに関するフォーラム   不均衡データ分類フレームワークにおけるサンプリング比率の最適化

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc. 

  11. オンラインプレゼンテーション賞

    2020.3   DEIM 2020   不均衡データ分類フレームワークにおけるサンプリング比率の最適化

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  12. FUJITSU賞

    2019.9   第12回Webとデータベースに関するフォーラム   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc. 

  13. マイクロアド賞

    2019.9   第12回Webとデータベースに関するフォーラム   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc. 

  14. 株式会社FRONTEO賞

    2019.9   第12回Webとデータベースに関するフォーラム   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc. 

  15. FUJITSU賞

    2019.9   WebDB Forum 2019   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  16. 株式会社FRONTEO賞

    2019.9   WebDB Forum 2019   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  17. マイクロアド賞

    2019.9   WebDB Forum 2019   弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  18. JURIX 2018 Best paper award

    2018.12   Japanese Legal Term Correction using Random Forests

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    Award type:Award from international society, conference, symposium, etc.  Country:Netherlands

  19. Best Paper Award

    2018.12   JURIX 2018   Japanese Legal Term Correction using Random Forests

    Takahiro Yamakoshi, Takahiro Komamizu, Yasuhiro Ogawa, Katsuhiko Toyama

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    Award type:Award from international society, conference, symposium, etc.  Country:Netherlands

  20. 優秀インタラクティブ賞

    2018.3   第10回データ工学と情報マネジメントに関するフォーラム   ノードがテキスト情報を持つ動的ネットワークにおけるノードと単語の分散表現学習

    伊藤 寛祥, 駒水 孝裕, 天笠 俊之, 北川 博之

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    Award type:Award from Japanese society, conference, symposium, etc. 

  21. 優秀インタラクティブ賞

    2018.3   DEIM 2018   ノードがテキスト情報を持つ動的ネットワークにおけるノードと単語の分散表現学習

    伊藤 寛祥, 駒水 孝裕, 天笠 俊之, 北川 博之

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  22. 学生プレゼンテーション賞

    2017.3   DEIM 2017   ノードが複数の属性を持つグラフにおけるコミュニティ検出

    伊藤 寛祥, 駒水 孝裕, 天笠 俊之, 北川 博之

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  23. 学生奨励賞

    2017.3   情報処理学会全国大会 2017   GitHubとStack Overflowにおけるユーザ行動の統一的な分析

    永野 真知, 早瀬 康裕, 駒水 孝裕, 北川 博之

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  24. iiWAS 2015 Best paper award

    2015.12   the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2015)  

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    Award type:Award from international society, conference, symposium, etc.  Country:Belgium

  25. 情報処理学会 第73回全国大会 学生奨励賞

    2011.3   情報処理学会  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  26. 山下記念研究賞

    2011.3   情報処理学会  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  27. 第2回データ工学と情報マネジメントに関するフォーラム 2010 学生奨励賞

    2010.3   第2回データ工学と情報マネジメントに関するフォーラム  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

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Papers 83

  1. Towards Ensemble-Based Imbalanced Text Classification Using Metric Learning.

    Takahiro Komamizu

    DEXA (2)   Vol. 14147 LNCS   page: 188 - 202   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1007/978-3-031-39821-6_15

    Scopus

    Other Link: https://dblp.uni-trier.de/db/conf/dexa/dexa2023-2.html#Komamizu23

  2. MMEnsemble: Imbalanced Classification Framework Using Metric Learning and Multi-sampling Ratio Ensemble.

    Takahiro Komamizu

    Database and Expert Systems Applications - 32nd International Conference   Vol. 12924   page: 176 - 188   2021

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-030-86475-0_18

    Web of Science

    Scopus

    Other Link: https://dblp.uni-trier.de/db/conf/dexa/dexa2021-2.html#Komamizu21

  3. Correction to: Computational measurement of perceived pointiness from pronunciation (Multimedia Tools and Applications, (2023), 83, 9, (26183-26210), 10.1007/s11042-023-15732-z)

    Matsuhira C., Kastner M.A., Komamizu T., Ide I., Hirayama T., Kawanishi Y., Doman K., Deguchi D.

    Multimedia Tools and Applications   Vol. 83 ( 9 ) page: 26211 - 26212   2024.3

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    Publisher:Multimedia Tools and Applications  

    The original publication of this article contains the following errors: missing ORCID of authors incorrect author contribution statement pronunciation symbols were not shown correctly in both online and PDF versionsthe gamma symbol "Γ" were incorrectly displayed as "0" in the PDF version missing ORCID of authors incorrect author contribution statement pronunciation symbols were not shown correctly in both online and PDF versions the gamma symbol "Γ" were incorrectly displayed as "0" in the PDF version The original article has been corrected.

    DOI: 10.1007/s11042-023-17657-z

    Scopus

  4. Image-Collection Summarization Using Scene-Graph Generation With External Knowledge

    Phueaksri, I; Kastner, MA; Kawanishi, Y; Komamizu, T; Ide, I

    IEEE ACCESS   Vol. 12   page: 17499 - 17512   2024

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    Publisher:IEEE Access  

    Summarization tasks aim to summarize multiple pieces of information into a short description or representative information. A text summarization task summarizes textual information into a short description, whereas an image collection summarization task summarizes an image collection into images or textual representation in which the challenge is to understand the relationship between images. In recent years, scene-graph generation has shown the advantage of describing the visual contexts of a single-image, and incorporating external knowledge into the scene-graph generation model has also given effective directions for unseen single-image scene-graph generation. While external knowledge has been implemented in related work, it is still challenging to use this information efficiently for relationship estimation during the summarization. Following this trend, in this paper, we propose a novel scene-graph-based image-collection summarization model that aims to generate a summarized scene-graph of an image collection. The key idea of the proposed method is to enhance the relation predictor toward relationships between images in an image collection incorporating knowledge graphs as external knowledge for training a model. With this approach, we build an end-to-end framework that can generate a summarized scene graph of an image collection. To evaluate the proposed method, we also build an extended annotated MS-COCO dataset for this task and introduce an evaluation process that focuses on estimating the similarity between a summarized scene graph and ground-truth scene graphs. Traditional evaluation focuses on calculating precision and recall scores, which involve true positive predictions without balancing precision and recall. Meanwhile, the proposed evaluation process focuses on calculating the F-score of the similarity between a summarized scene graph and ground-truth scene graphs, which aims to balance both false positives and false negatives. Experimental results show that using external knowledge to enhance the relation predictor achieves better results than existing methods.

    DOI: 10.1109/ACCESS.2024.3360113

    Web of Science

    Scopus

  5. RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network

    Shi J., Komamizu T., Doman K., Kyutoku H., Ide I.

    Proceedings of the 5th ACM International Conference on Multimedia in Asia, MMAsia 2023     2023.12

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    Publisher:Proceedings of the 5th ACM International Conference on Multimedia in Asia, MMAsia 2023  

    Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation.

    DOI: 10.1145/3595916.3626430

    Scopus

  6. NarSUM 2023 Chairs Welcome

    Kankanhalli M.S., Patras I., Liu J., Wong Y., Komamizu T.

    NarSUM 2023 - Proceedings of the 2nd Workshop on User-centric Narrative Summarization of Long Videos, Co-located with: MM 2023     2023.10

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    Publisher:NarSUM 2023 - Proceedings of the 2nd Workshop on User-centric Narrative Summarization of Long Videos, Co-located with: MM 2023  

    Scopus

  7. NarSUM '23: The 2nd Workshop on User-Centric Narrative Summarization of Long Videos

    Kankanhalli M.S., Patras I.Y., Liu J., Wong Y., Komamizu T., Yamazaki S., Stephen K., Kansal K.

    MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia     page: 9731 - 9733   2023.10

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    With video capture devices becoming widely popular, the amount of video data generated per day has seen a rapid increase over the past few years. Browsing through hours of video data to retrieve useful information is a tedious and boring task. Video Summarization technology has played a crucial role in addressing this issue. It is a well-researched topic in the multimedia community. However, the focus so far has been limited to creating summary to videos which are short (only a few minutes). This workshop aims to call for researchers on relevant background to focus on novel solutions for user-centric narrative summarization of long videos. This workshop will also cover important aspects of video summarization research like what is "important"in a video, how to evaluate the goodness of a created summary, open challenges in video summarization, etc.

    DOI: 10.1145/3581783.3610946

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  8. An Automatic Labeling Method for Subword-Phrase Recognition in Effective Text Classification

    Yusuke Kimura, Takahiro Komamizu, Kenji Hatano

    Informatica   Vol. 47 ( 3 ) page: 315 - 326   2023.8

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    Publishing type:Research paper (scientific journal)   Publisher:Slovenian Association Informatika  

    DOI: 10.31449/inf.v47i3.4742

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  9. Computational measurement of perceived pointiness from pronunciation

    Matsuhira, C; Kastner, MA; Komamizu, T; Ide, I; Hirayama, T; Kawanishi, Y; Doman, K; Deguchi, D

    MULTIMEDIA TOOLS AND APPLICATIONS   Vol. 83 ( 9 ) page: 26183 - 26210   2023.8

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    Sound symbolism is a well-researched topic of psycholinguistics, which tries to comprehend the connection between the sound of a word and its meanings. The Bouba-Kiki effect, one form of sound symbolism, claims that people perceive the pronunciation of “Kiki” as pointier than that of “Bouba.” There is no research that focuses on modeling such perception, i.e., how pointy a pronunciation sounds to humans, through computational and data-driven approaches. To address this, this paper first proposes the novel concept of “phonetic pointiness” defined as how pointy a shape humans are most likely to associate with a given pronunciation. We then model this phonetic pointiness from computational and data-driven approaches to calculate a score for an arbitrary pronunciation. There are three proposed models: a referential model, an expressive model, and a combined model, which integrates the previous two. The idea comes from an existing psycholinguistic classification of two types of sound symbolisms: referential symbolism and expressive symbolism, where the former relates to vocabulary knowledge, while the latter is based on pure human intuition. The proposed models are constructed only with image and language data available on the Web, therefore not requiring task-specific human annotations. We evaluate these models through a crowd-sourced user study, finding a promising correlation between human perception and the phonetic pointiness calculated by the proposed models. The results indicate that human perception can be modeled better by combining both types of sound symbolisms. Furthermore, by observing the behaviors of the models, we show several possible use-cases, such as product naming and psycholinguistic research, which can be a useful insight to further studies and applications.

    DOI: 10.1007/s11042-023-15732-z

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  10. Image Impression Estimation by Clustering People with Similar Tastes

    Kojima Banri, Komamizu Takahiro, Kawanishi Yasutomo, Doman Keisuke, Ide Ichiro

    IEICE Proceeding Series   Vol. 78   page: P1-14   2023.7

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    This paper proposes a method for estimating impressions received from images according to the personal attributes of users, so that they can find the desired images based on their tastes. A previous study taking into account gender and age as personal attributes showed promising results. However, it also showed that users sharing the same gender and age do not necessarily share similar tastes. Therefore, other attributes should be considered to well capture users' personal tastes. However, taking more attributes into account leads to a problem that insufficient amounts of data are served to classifiers, due to explosion of the number of combinations of attributes. To tackle this problem, we propose an aggregation-based method to condense training data for impression estimation while personal attribute information is taken into account. For evaluation, a dataset of 4,000 carpet images annotated with 24 impression words by crowd-workers was prepared, which contained 273k annotations. Experimental results showed that the use of combinations of personal attributes improved the accuracy of impression estimation. This indicates that combinations of personal attributes are helpful to estimate impressions of individual viewers to images.

    DOI: 10.34385/proc.78.p1-14

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  11. Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection

    Padeiro Carlos-Victorino, Komamizu Takahiro, Ide Ichiro

    IEICE Proceeding Series   Vol. 78   page: P1-23   2023.7

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    Agriculture is the pillar industry of human sur- vival. However, various crop diseases reduce the hu- man food supply and lead to starvation and death in the worst cases. Experts perform visual symptoms ob- servation for crop disease diagnosis. Which process is time-consuming and expensive. Also, the process has significant risk of human error due to subjective per- ception. Convolutional Neural Networks (CNN) use image processing techniques to show great potential in plant disease detection. However, it requires thou- sands of channels to learn rich features, resulting in large models requiring powerful computing, power sup- ply, and high bandwidth, making it more expensive and difficult for farmers to acquire. Therefore, deploying these solutions on resource-constrained devices is de- sirable to make them more accessible. Thus, we pro- pose a lightweight object detection CNN that can run on resource-constrained devices to detect crop diseases. Channel pruning is applied to optimize resource use by removing unimportant channels and filter weights to reduce network parameters, inference time, and the number of FLOPS. Experimental results with object de- tector, Faster R-CNN with two backbones, ResNet-50, and EfficientNet-B7, show significant improvement in model efficiency, keeping high accuracy.

    DOI: 10.34385/proc.78.p1-23

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  12. Small Object Detection for Birds with Swin Transformer

    Huo Da, Kastner Marc-A., Liu Tingwei, Kawanishi Yasutomo, Hirayama Takatsugu, Komamizu Takahiro, Ide Ichiro

    IEICE Proceeding Series   Vol. 78   page: TE-3   2023.7

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    Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or far objects in remote sensing scenarios. However, when the target object is small and sparse, there is a lack of objects available for training, making it more difficult to learn effective features. In this paper, we propose a specialized method for detecting a specific category of small objects; birds. Particularly, we improve the features learned by the neck; the sub-network between the backbone and the prediction head, to learn more effective features with a hierarchical design. We employ Swin Transformer to upsample the image features. Moreover, we change the shifted window size for adapting to small objects. Experiments show that the proposed Swin Transformerbased neck combined with CenterNet can lead to good performance by changing the window sizes. We further find that smaller window sizes (default 2) benefit mAPs for small object detection.

    DOI: 10.34385/proc.78.te-3

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  13. MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

    Kondo Yuki, Ukita Norimichi, Yamaguchi Takayuki, Hou Hao-Yu, Shen Mu-Yi, Hsu Chia-Chi, Huang En-Ming, Huang Yu-Chen, Xia Yu-Cheng, Wang Chien-Yao, Lee Chun-Yi, Huo Da, Kastner Marc-A., Liu Tingwei, Kawanishi Yasutomo, Hirayama Takatsugu, Komamizu Takahiro, Ide Ichiro, Shinya Yosuke, Liu Xinyao, Liang Guang, Yasui Syusuke

    IEICE Proceeding Series   Vol. 78   page: TE-1   2023.7

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    Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the awardwinning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.

    DOI: 10.34385/proc.78.te-1

    CiNii Research

  14. [D21] Construction of Law Database for History Information

    SANO Tomoya, TOYAMA Katsuhiko, KOMAMIZU Takahiro, MASUDA Tomoko

    Dejitaru Akaibu Gakkaishi   Vol. 7 ( s2 ) page: s142 - s145   2023

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    Language:Japanese   Publisher:Japan Society for Digital Archive  

    DOI: 10.24506/jsda.7.s2_s142

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  15. Visual Passage Score Aggregation for Image Retrieval

    Komamizu T.

    Proceedings - 2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval, MIPR 2023     page: 37 - 42   2023

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    Publisher:Proceedings - 2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval, MIPR 2023  

    This paper proposes an effective image retrieval method. Recent image retrieval approaches attempt to construct a single global feature, including local features of an image. In contrast, this paper proposes multiple features for each image. The basic idea is that a target object in a query image is not necessarily in a major part of a database image; therefore, its single feature may include noisy information from the surroundings of the target object. To deal with this, this paper proposes a Visual Passage Score Aggregation framework (VPSA). VPSA first decomposes an image into several pieces of images, called Visual Passages. Based on visual passages, VPSA aggregates relevance scores of visual passages for ranking. VPSA is efficient in the retrieval phase because an ordinary nearest neighbor search is used. The experiment revealed that VPSA showed superior or comparable performance to the state-of-the-art methods, and it takes a shorter time in the retrieval phase.

    DOI: 10.1109/MIPR59079.2023.00021

    Scopus

  16. Towards Captioning an Image Collection from a Combined Scene Graph Representation Approach

    Itthisak Phueaksri, Marc A. Kastner, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide

    MultiMedia Modeling   Vol. 13833   page: 178 - 190   2023

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    Publishing type:Part of collection (book)   Publisher:Springer International Publishing  

    DOI: 10.1007/978-3-031-27077-2_14

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  17. Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection.

    Carlos Victorino Padeiro, Takahiro Komamizu, Ichiro Ide

    MVA     page: 1 - 6   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.23919/MVA57639.2023.10215815

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    Other Link: https://dblp.uni-trier.de/db/conf/mva/mva2023.html#PadeiroKI23

  18. Small Object Detection for Birds with Swin Transformer.

    Da Huo, Marc A. Kastner 0001, Tingwei Liu, Yasutomo Kawanishi, Takatsugu Hirayama, Takahiro Komamizu, Ichiro Ide

    MVA     page: 1 - 5   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.23919/MVA57639.2023.10216093

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    Other Link: https://dblp.uni-trier.de/db/conf/mva/mva2023.html#HuoKLKHKI23

  19. Nonword-to-Image Generation Considering Perceptual Association of Phonetically Similar Words.

    Chihaya Matsuhira, Marc A. Kastner 0001, Takahiro Komamizu, Takatsugu Hirayama, Keisuke Doman, Ichiro Ide

    MCGE@MM     page: 115 - 125   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1145/3607541.3616818

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    Other Link: https://dblp.uni-trier.de/db/conf/mcge/mcge2023.html#Matsuhira0KHDI23

  20. MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results.

    Yuki Kondo, Norimichi Ukita, Takayuki Yamaguchi, Hao-Yu Hou, Mu-Yi Shen, Chia-Chi Hsu, En-Ming Huang, Yu-Chen Huang, Yu-Cheng Xia, Chien-Yao Wang, Chun-Yi Lee, Da Huo, Marc A. Kastner 0001, Tingwei Liu, Yasutomo Kawanishi, Takatsugu Hirayama, Takahiro Komamizu, Ichiro Ide, Yosuke Shinya, Xinyao Liu, Guang Liang, Syusuke Yasui

    CoRR   Vol. abs/2307.09143   2023

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.48550/arXiv.2307.09143

  21. MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results.

    Yuki Kondo, Norimichi Ukita, Takayuki Yamaguchi, Hao-Yu Hou, Mu-Yi Shen, Chia-Chi Hsu, En-Ming Huang, Yu-Chen Huang, Yu-Cheng Xia, Chien-Yao Wang, Chun-Yi Lee, Da Huo, Marc A. Kastner 0001, Tingwei Liu, Yasutomo Kawanishi, Takatsugu Hirayama, Takahiro Komamizu, Ichiro Ide, Yosuke Shinya, Xinyao Liu, Guang Liang, Syusuke Yasui

    MVA     page: 1 - 11   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.23919/MVA57639.2023.10215935

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    Other Link: https://dblp.uni-trier.de/db/conf/mva/mva2023.html#KondoUYHSHHHXWLHKLKHKISLLY23

  22. IPA-CLIP: Integrating Phonetic Priors into Vision and Language Pretraining.

    Chihaya Matsuhira, Marc A. Kastner 0001, Takahiro Komamizu, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide

    CoRR   Vol. abs/2303.03144   2023

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    DOI: 10.48550/arXiv.2303.03144

  23. Image Impression Estimation by Clustering People with Similar Tastes.

    Banri Kojima, Takahiro Komamizu, Yasutomo Kawanishi, Keisuke Doman, Ichiro Ide

    MVA     page: 1 - 5   2023

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.23919/MVA57639.2023.10216055

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    Other Link: https://dblp.uni-trier.de/db/conf/mva/mva2023.html#KojimaKKDI23

  24. An Approach to Generate a Caption for an Image Collection Using Scene Graph Generation

    Phueaksri, I; Kastner, MA; Kawanishi, Y; Komamizu, T; Ide, I

    IEEE ACCESS   Vol. 11   page: 128245 - 128260   2023

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    Summarization is a challenging task that aims to generate a summary by grasping common information of a given set of information. Text summarization is a popular task of determining the topic or generating a textual summary of documents. In contrast, image summarization aims to find a representative summary of a collection of images. However, current methods are still restricted to generating a visual scene graph, tags, and noun phrases, but cannot generate a fitting textual description of an image collection. Thus, we introduce a novel framework for generating a summarized caption of an image collection. Since scene graph generation shows advancement in describing objects and their relationships on a single image, we use it in the proposed method to generate a scene graph for each image in an image collection. Then, we find common objects and their relationships from all scene graphs and represent them as a summarized scene graph. For this, we merge all scene graphs and select part of it by estimating the most common objects and relationships. Finally, the summarized scene graph is input into a captioning model. In addition, we introduce a technique to generalize specific words in the final caption into common concept words incorporating external knowledge. To evaluate the proposed method, we construct a dataset for this task by extending the annotation of the MS-COCO dataset using an image retrieval method. The evaluation of the proposed method on this dataset showed promising performance compared to text summarization-based methods.

    DOI: 10.1109/ACCESS.2023.3332098

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  25. Multi-task Learning-based Text Classification with Subword-Phrase Extraction

    Yusuke Kimura, Takahiro Komamizu, Kenji Hatano

    The 11th International Symposium on Information and Communication Technology     page: 23 - 30   2022.12

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    Publishing type:Research paper (international conference proceedings)   Publisher:ACM  

    DOI: 10.1145/3568562.3568635

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  26. Detection of Birds in a 3D Environment Referring to Audio-Visual Information.

    Yasutomo Kawanishi, Ichiro Ide, Baidong Chu, Chihaya Matsuhira, Marc A. Kastner 0001, Takahiro Komamizu, Daisuke Deguchi

    18th IEEE International Conference on Advanced Video and Signal Based Surveillance(AVSS)     page: 1 - 7   2022

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/AVSS56176.2022.9959510

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    Other Link: https://dblp.uni-trier.de/db/conf/avss/avss2022.html#KawanishiICMKKD22

  27. Intuitive Gait Modeling using Mimetic-Words for Gait Description and Generation.

    Hirotaka Kato, Takatsugu Hirayama, Keisuke Doman, Ichiro Ide, Yasutomo Kawanishi, Takahiro Komamizu, Daisuke Deguchi, Hiroshi Murase

    5th IEEE International Conference on Multimedia Information Processing and Retrieval(MIPR)     page: 240 - 245   2022

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/MIPR54900.2022.00050

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    Other Link: https://dblp.uni-trier.de/db/conf/mipr/mipr2022.html#KatoHDIKKDM22

  28. Towards Efficient Data Access Through Multiple Relationship in Graph-Structured Digital Archives

    Kazuma Kusu, Takahiro Komamizu, Kenji Hatano

    From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries   Vol. 13636   page: 377 - 391   2022

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    Publishing type:Part of collection (book)   Publisher:Springer International Publishing  

    DOI: 10.1007/978-3-031-21756-2_29

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  29. Action Semantic Alignment for Image Captioning.

    Da Huo, Marc A. Kastner 0001, Takahiro Komamizu, Ichiro Ide

    5th IEEE International Conference on Multimedia Information Processing and Retrieval(MIPR)     page: 194 - 197   2022

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/MIPR54900.2022.00041

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    Other Link: https://dblp.uni-trier.de/db/conf/mipr/mipr2022.html#HuoKKI22

  30. An Ensemble Framework of Multi-ratio Undersampling-based Imbalanced Classification Reviewed

    駒水 孝裕

    Journal of Data Intelligence   Vol. 2 (1)   page: 30 - 46   2021

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  31. FPX-G: First Person Exploration for Graph.

    Takahiro Komamizu, Shoi Ito, Yasuhiro Ogawa, Katsuhiko Toyama

    4th IEEE International Conference on Multimedia Information Processing and Retrieval(MIPR)     page: 70 - 76   2021

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/MIPR51284.2021.00018

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    Other Link: https://dblp.uni-trier.de/db/conf/mipr/mipr2021.html#KomamizuIOT21

  32. Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes

    Yamakoshi T., Komamizu T., Ogawa Y., Toyama K.

    WAT 2021 - 8th Workshop on Asian Translation, Proceedings of the Workshop     page: 124 - 132   2021

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    For updating the translations of Japanese statutes based on their amendments, we need to consider the translation “focality;” that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing its contents. In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations. Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT). ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference. This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions. We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not. With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.

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  33. Combining Multi-ratio Undersampling and Metric Learning for Imbalanced Classification.

    Takahiro Komamizu

    Journal of Data Intelligence   Vol. 2 ( 4 ) page: 462 - 474   2021

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.26421/JDI2.4-5

  34. Random walk-based entity representation learning and re-ranking for entity search.

    Takahiro Komamizu

    Knowledge and Information Systems   Vol. 62 ( 8 ) page: 2989 - 3013   2020.8

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    DOI: 10.1007/s10115-020-01445-4

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  35. Japanese Legal Term Correction using Random Forest

    Yamakoshi Takahiro, Ogawa Yasuhiro, Komamizu Takahiro, Toyama Katsuhiko

    Transactions of the Japanese Society for Artificial Intelligence   Vol. 35 ( 1 ) page: H-J53_1 - 14   2020.1

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    <p>We propose a method that assists legislation drafters in finding inappropriate use of Japanese legal terms and their corrections from Japanese statutory sentences. In particular, we focus on sets of similar legal terms whose usages are strictly defined in legislation drafting rules that have been established over the years. In this paper, we first define input and output of legal term correction task. We regard it as a special case of sentence completion test with multiple choices. Next, we describe a legal term correction method for Japanese statutory sentences. Our method predicts suitable legal terms using Random Forest classifiers. The classifiers in our method use adjacent words to a target legal term as input features, and are optimized in various parameters including the number of adjacent words to be used for each legal term set. We conduct an experiment using actual statutory sentences from 3,983 existing acts and cabinet orders that consist of approximately 47M words in total. As for legal term sets, we pick 27 sets from legislation drafting manuals. The experimental result shows that our method outperformed existing modern word prediction methods using neural language models and that each Random Forest classifier utilizes characteristics of its corresponding legal term set.</p>

    DOI: 10.1527/tjsai.h-j53

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    CiNii Research

  36. Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier

    Transactions of the Japanese Society for Artificial Intelligence   Vol. 35 ( 4 ) page: 1 - 17   2020

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    <p>We propose a method to assist legislative drafters that locates inappropriate legal terms in Japanese statutorysentences and suggests corrections. We focus on sets of mistakable legal terms whose usages are defined in legislationdrafting rules. Our method predicts suitable legal terms using a classifier based on BERT (Bidirectional EncoderRepresentations from Transformers). The BERT classifier is pretrained with a huge number of whole sentences; thus,it contains abundant linguistic knowledge. Classifiers for predicting legal terms suffer from two-level infrequency:term-level infrequency and set-level infrequency. The former causes a class imbalance problem and the latter causesan underfitting problem; both degrade classification performance. To overcome these problems, we apply threetechniques, namely, preliminary domain adaptation, repetitive soft undersampling, and classifier unification. Thepreliminary domain adaptation improves overall performance by providing prior knowledge of statutory sentences,the repetitive soft undersampling overcomes term-level infrequency, and the classifier unification overcomes set-levelinfrequency while saving storage consumption. Our experiments show that our classifier outperforms conventionalclassifiers using Random Forest or language models, and that all three training techniques improve performance.</p>

    DOI: 10.1527/tjsai.E-K25

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  37. Japanese Legal Term Correction using BERT Pretrained Model

    YAMAKOSHI Takahiro, KOMAMIZU Takahiro, OGAWA Yasuhiro, TOYAMA Katsuhiko

    Proceedings of the Annual Conference of JSAI   Vol. 2020 ( 0 ) page: 4P3OS805 - 4P3OS805   2020

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    Publisher:The Japanese Society for Artificial Intelligence  

    <p>Legal documents contain legal terms that have similar meaning or pronunciation each other. Japanese legislation defines their usage on the basis of traditional customs and rules. In accordance with the definition, we need to use these legal terms properly and strictly in a statute. We are also encouraged to follow the definition in writing broad-sense legal documents, such as contracts and terms of use. To assist in writing legal documents, we propose a method that locates inappropriate legal terms in Japanese statutory sentences and suggests corrections. We solve this task with a classifier by regarding the task as a sentence completion test. Our classifier is based on a pretrained BERT model trained by using a large amount of general sentences. To raise performance, we apply three training techniques: domain adaptation, undersampling, classifier unification. Our experiments show that our classifier achieved better performance than Random Forest-based ones and language model-based ones.</p>

    DOI: 10.11517/pjsai.JSAI2020.0_4P3OS805

  38. SPARQL with XQuery-based filtering

    Komamizu T.

    CEUR Workshop Proceedings   Vol. 2721   page: 69 - 73   2020

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    Linked Open Data (LOD) has been proliferated over various domains, however, there are still lots of open data in various format other than RDF. Document-centric XML data are such open data that are connected with entities in LOD as supplemental documents for these entities. To utilize document-centric XML data linked from entities in LOD, in this paper, a SPARQL-based seamless access method on RDF and XML data is proposed. In particular, an extension to SPARQL, XQueryFILTER, which enables XQuery as a filter in SPARQL is proposed. For efficient query processing of the combination of SPARQL and XQuery, a query optimization is proposed. Experimental scenarios using real-world data showcase the effectiveness of XQueryFILTER and optimization efficiency.

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  39. MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data.

    Takahiro Komamizu, Risa Uehara, Yasuhiro Ogawa, Katsuhiko Toyama

    Database and Expert Systems Applications - 31st International Conference   Vol. 12392   page: 213 - 228   2020

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-030-59051-2_14

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    Other Link: https://dblp.uni-trier.de/db/conf/dexa/dexa2020-2.html#KomamizuUOT20

  40. Exploring Relevant Parts between Legal Documents using Substructure Matching Reviewed

    Takahiro Komamizu, Kazuya Fujioka, Yasuhiro Ogawa, Katsuhiko Toyama

    Proceeding of the Thirteenth International Workshop on Juris-informatics (JURISIN 2019)   Vol. 12331   page: 5 - 19   2020

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    Language:English   Publishing type:Research paper (other academic)   Publisher:Springer  

    DOI: 10.1007/978-3-030-58790-1_1

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    Other Link: https://dblp.uni-trier.de/db/conf/jsai/jsai2019w.html#KomamizuFOT19

  41. SPARQL with XQuery-based Filtering.

    Takahiro Komamizu

    CoRR   Vol. abs/2009.06194   2020

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    Other Link: https://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-06194

  42. Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier" Reviewed

    Takahiro Yamakoshi, Takahiro Komamizu, Yasuhiro Ogawa, Katsuhiko Toyama

    the 3rd Annual Workshop on Applications of Artificial Intelligence in the Legal Industry (LegalAI 2019)     page: 4342-4351   2019.12

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  43. Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier Matching Reviewed

    Takahiro Yamakoshi, Takahiro Komamizu, Yasuhiro Ogawa, Katsuhiko Toyama

    Proc. 3rd Annual Workshop on Applications of Artificial Intelligence in the Legal Industry     page: - - 4351   2019.12

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    Language:English   Publishing type:Research paper (other academic)   Publisher:IEEE  

    DOI: 10.1109/BigData47090.2019.9006511

    Scopus

    Other Link: https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2019.html#YamakoshiKOT19

  44. Exploring Relevant Parts between Legal Documents using Substructure Matching Reviewed

    Takahiro Komamizu, Kazuya Fujioka, Yasuhiro Ogawa, Katsuhiko Toyama

    the Thirteenth International Workshop on Juris-informatics (JURISIN 2019)     page: 16-28   2019.11

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    Authorship:Lead author   Language:English  

  45. 言い換えによる自然言語-SPARQL対訳コーパスの拡張

    李偉嘉, 小川泰弘, 駒水孝裕, 外山勝彦

    第17回情報学ワークショップ論文集     page: -   2019.11

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    Language:Japanese   Publishing type:Research paper (other academic)  

  46. Analyzing Japanese Law History through Modeling Multi-versioned Entity Reviewed

    Takahiro Komamizu, Yushi Uchida, Yasuhiro Ogawa, Katsuhiko Toyama

    the 2nd International Workshop on Contextualized Knowledge Graphs (CKG 2019)     page: -   2019.10

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    Authorship:Lead author   Language:English  

  47. 利用規約中の不公平文の自動検出

    青山恵子, 小川泰弘, 駒水孝裕, 外山勝彦

    第15回テキストアナリティクス・シンポジウム NLC2019-8(2019-9)     page: 1-6   2019.9

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    Language:Japanese   Publishing type:Research paper (other academic)  

  48. Thai Legal Term Correction using Random Forests with Outside-the-sentence Features Reviewed

    Takahiro Yamakoshi, Vee Satayamas, Hutchatai Chanlekha, Yasuhiro Ogawa, Takahiro Komamizu, Asanee Kawtrakul, Katsuhiko Toyama

    the 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC 33)     page: 161-170   2019.9

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    Language:English  

  49. 弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク Reviewed

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    第12回Webとデータベースに関するフォーラム     page: 81-84   2019.9

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    Language:Japanese  

  50. 共通BERT分類器による紛らわしい法令用語の校正

    山腰貴大, 駒水孝裕, 小川泰弘, 外山勝彦

    言語処理学会NLP若手の会第14回シンポジウム     page: -   2019.8

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    Language:Japanese   Publishing type:Research paper (other academic)  

  51. nagoy Team's Summarization System at the NTCIR-14 QA-Lab PoliInfo Reviewed

    Yasuhiro Ogawa, Michiaki Satou, Takahiro Komamizu, Katsuhiko Toyama

    the Fourteenth NTCIR conference (NTCIR-14) , Revised Selected Papers     page: 110-121   2019.6

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    Language:English  

  52. Extracting Important Sentences with Random Forest for Statute Summarization

    OGAWA Yasuhiro, SATOU Michiaki, KOMAMIZU Takahiro, TOYAMA Katsuhiko

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2019 ( 0 ) page: 4E2OS7a02 - 4E2OS7a02   2019.6

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japanese Society for Artificial Intelligence  

    Our purpose is to provide an automatic summarization for Japanese acts and we propose a sententence extraction method with Random Forest. While the traditional automatic summarization methods have used the information of summarizing source data, in recent years, the methods based on machine learning use the summarization results. However, in such a method, the amount of learning corpus is small, especially in Japanese text. In this research, we solve this problem by using "Outlines of Japanese Statutes," which are official summaries of statutes published by the Japanese government. Furthermore, we show that the sentence extraction method with Random Forest has higher performance rather than with decision trees or with support vector machines.

    DOI: 10.11517/pjsai.jsai2019.0_4e2os7a02

    CiNii Research

  53. Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs. Reviewed

    Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    IEICE Trans. Inf. Syst.   Vol. E102D ( 4 ) page: 810 - 820   2019.4

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:The Institute of Electronics, Information and Communication Engineers  

    <p>Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves <u><b>C</b></u>ommunity detection, <u><b>A</b></u>ttribute-value clustering, and deriving <u><b>R</b></u>elationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.</p>

    DOI: 10.1587/transinf.2018DAP0022

    Web of Science

    Scopus

    CiNii Research

  54. Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier

    Yamakoshi, T; Komamizu, T; Ogawa, Y; Toyama, K

    2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)     page: 4342 - 4351   2019

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  55. nagoy Team's Summarization System at the NTCIR-14 QA Lab-PoliInfo Reviewed

    Yasuhiro Ogawa, Michiaki Satou, Takahiro Komamizu, Katsuhiko Toyama

    Post-conference Proceedings of the 14th NTCIR Conference on Evaluation of Information Access Technologies   Vol. 11966 LNCS   page: to appear - 121   2019

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    Language:English   Publishing type:Research paper (other academic)   Publisher:Springer  

    DOI: 10.1007/978-3-030-36805-0_9

    Scopus

    Other Link: https://dblp.uni-trier.de/db/conf/ntcir/ntcir2019.html#OgawaSKT19

  56. Graph Analytical Re-ranking for Entity Search

    Komamizu T.

    CEUR Workshop Proceedings   Vol. 2482   2019

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    Entity search is a fundamental task in Linked Data (LD). The task is, given a keyword search query, to retrieve a set of entities in LD which are relevant to the query. The state-of-the-art approaches for entity search are based on information retrieval technologies such as TF-IDF vectorization and ranking models. This paper examines the approaches by applying a traditional evaluation metrics, recall@k, and shows ranking qualities still room left for improvements. In order to improve the ranking qualities, this paper explores possibilities of graph analytical methods. LD is regarded as a large graph, graph analytical approaches are therefore appropriate for this purpose. Since query-based graph analytical approaches fit to entity search tasks, this paper proposes a personalized PageRank-based re-ranking method, PPRSD (Personalized PageRank based Score Distribution), for retrieved results by the state-of-the-art. The experimental evaluation recognizes improvements but its results are not satisfactory, yet. For further improvements, this paper reports investigations about relationship between queries and entities in terms of path lengths on the graph, and discusses future directions for graph analytical approaches.

    Scopus

  57. Analyzing Japanese law history through modeling multi-versioned entity

    Komamizu T., Uchida Y., Ogawa Y., Toyama K.

    CEUR Workshop Proceedings   Vol. 2599   2019

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    Publisher:CEUR Workshop Proceedings  

    As law is a blueprint of a society and is changed over time as social environments changed, analyzing histories (change provenances) of laws can reveal important facts such as legislative facts and critical events for the society. Linked Open Data (LOD) has emerged as a preferred method for publishing and sharing open data, however, there is an ontological barrier for publishing law history data as LOD. To break through the barrier, this paper proposes an ontology for law history data of the Japanese statute law. The ontology is inspired from PROV-O and SIOC ontologies. The LOD dataset based on the proposed ontology enables wide variety of analyses on the law history data by simple SPARQL queries. The analyses include simple search, visualization, temporal analysis, data mining, etc. This paper displays parts of the analyses which indicate several legislative facts behind changes of laws. The analyses demonstrate the proposed ontology and LOD dataset are useful for legal data analysis. The proposed ontology is comparable with ELI (European Legislation Identifier) which is designed for EU laws, this paper thus discusses the comparability and future directions of the proposed ontology.

    Scopus

  58. Thai legal term correction using random forests with outside-the-sentence features

    Yamakoshi T., Satayamas V., Chanlekha H., Ogawa Y., Komamizu T., Kawtrakul A., Toyama K.

    Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019     page: 279 - 287   2019

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    Publisher:Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019  

    We propose a method for finding and correct- ing misused Thai legal terms in Thai statu- tory sentences. Our method predicts legal terms using Random Forest classifiers, each of which is optimized for each set of similar legal terms. Each classifier utilizes outside- the-sentence features, namely, promulgation year, title keywords, and section keywords of statutes, in addition to words adjacent to the targeted legal term. Our experiment shows that our method outperformed not only a Ran- dom Forest method without the outside-the- sentence features, but also BERT (Bidirec- tional Encoder Representations from Trans- formers), a powerful language representation model, in overall accuracy.

    Scopus

  59. Graph Analytical Re-ranking for Entity Search

    Takahiro Komamizu

    Proceedings of the 1st International Workshop on EntitY REtrieval (EYRE 2018)     page: (to appear)   2018.10

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    Language:English  

  60. Learning Interpretable Entity Representation in Linked Data Reviewed

    Takahiro Komamizu

    Proc. the 29th International Conference on Database and Expert Systems Applications     page: 153-168   2018.9

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    Authorship:Lead author   Language:English  

  61. Community Detection and Correlated Attribute Cluster Analysis on Multi-Attributed Graphs Reviewed

    Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 2nd International workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP 2018) co-located with the 21st International Conference on Extending Database Technology (EDBT 2018)     2018.3

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    Language:English  

    DOI: 2-9

  62. Network-Word Embedding for Dynamic Text Attributed Networks Reviewed

    Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 6th International Workshop on Semantic Computing for Social Networks and Organization Sciences (SCSN 2018) co-located with the 12th IEEE International Conference on Semantic Computing (ICSC 2018)     2018.1

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    Language:English  

    DOI: 334-339

  63. Analytical toolbox for smart city applications: Garbage collection log use case Reviewed

    Takahiro Komamizu, Jin Nakazawa, Toshiyuki Amagasa, Hiroyuki Kitagawa, Hideyuki Tokuda

    Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017   Vol. 2018-   page: 4105 - 4110   2018.1

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Institute of Electrical and Electronics Engineers Inc.  

    Analyzing and feeding back the results on real-world services are important missions in the Big Data era to realize smart city. However, analyzing real-world data is still challenging because of dirtiness of data and large variety of analytic requirements. To cope with the challenges, this paper proposes and develops an analytical toolbox for smart city applications. The analytical toolbox consists of three phases: preparation, analysis, and visualization. The preparation phase deals with the dirtiness of the data by including fundamental data cleansing techniques and data integration techniques. The analysis phase is responsible for ETL (extract, transform and load) process and analytical query processing from the next phase. The visualization phase deals with analytical requirements from users and visualization of analytical results. This paper showcases a real-world use case of the proposed analytical toolbox. The use case is now open in public with help of Fujisawa city, Japan, and this fact indicates that the proposed analytical toolbox is feasible for real-world data analysis and feeding back to citizens.

    DOI: 10.1109/BigData.2017.8258429

    Scopus

  64. Implicit order join: Joining log data with property data by discovering implicit order-oriented keys with human assistance Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017   Vol. 2018-   page: 4400 - 4406   2018.1

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Institute of Electrical and Electronics Engineers Inc.  

    Data integration is still laboursome task when integrating data are not consistently managed. Such inconsistency can happen easily in real-world situations, such as properties of objects are managed by a central organization and trajectories (or logs) of the objects are recorded by other peripheral organizations. This paper deals with a case of missing ordering information. Integrating property data and log data without ordering information causes duplicated results. In order to solve this problem, this paper proposes a join algorithm, called implicit order join, which discovers implicit ordering information from both property data and log data with help of partial true integrated results from human assistance. With the discovered ordering information, the implicit order join enables to integrate the property data and log data. In order to discover the implicit ordering information, ordering correlation between attribute sequences of property data and log data should be found from comprehensive examination of possible attribute sequence pairs. The potential number of sequence pairs is as high as factorial order of the number of attributes. Therefore, this paper develops a heuristic approach to prune unnecessary examinations based on ordering dependency between attribute sequences. Experimental evaluation in this paper indicates that implicit order join can reduce 77% labouring tasks for integration and the pruning method reduces the number of attribute sequences in orders of magnitude.

    DOI: 10.1109/BigData.2017.8258474

    Scopus

  65. Japanese Legal Term Correction Using Random Forests Reviewed

    Takahiro Yamakoshi, Takahiro Komamizu, Yasuhiro Ogawa, Katsuhiko Toyama

    LEGAL KNOWLEDGE AND INFORMATION SYSTEMS (JURIX 2018)   Vol. 313   page: 161 - 170   2018

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  66. Learning Interpretable Entity Representation in Linked Data.

    Takahiro Komamizu

    Database and Expert Systems Applications - 29th International Conference   Vol. 11029   page: 153 - 168   2018

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-319-98809-2_10

    Web of Science

    Scopus

    Other Link: https://dblp.uni-trier.de/db/conf/dexa/dexa2018-1.html#Komamizu18

  67. CROISSANT: Centralized Relational Interface for Web-scale SPARQL Endpoints Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 19th International Conference on Information Integration and Web-based Applications & Services (iiWAS2017)     2017.12

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    Authorship:Lead author   Language:English  

    DOI: 284-288

  68. SOLA: Stream OLAP-based Analytical Framework for Roadway Maintenance Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Salman Ahmed Shaikh, Hiroaki Shiokawa, Hiroyuki Kitagawa

    Proc. the 9th International Conference on Management of Digital EcoSystems (MEDES 2017)     2017.11

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    Authorship:Lead author   Language:English  

    DOI: 35-42

  69. GitHubとStack Overflowの開発者の活動記録を併用したリポジトリ推薦 Reviewed

    永野 真知, 早瀬 康裕, 駒水 孝裕, 北川 博之

    ソフトウェアエンジニアリングシンポジウム 2017論文集     2017.8

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    Language:Japanese  

    DOI: 138-145

  70. Exploring identical users on GitHub and stack overflow Reviewed

    Takahiro Komamizu, Yasuhiro Hayase, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE     page: 584 - 589   2017

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Knowledge Systems Institute Graduate School  

    Analyzing behaviours of developers in different platforms (in particular, GitHub and Stack Overflow in this paper) can reveal interesting facts related to development activities. There are only few datasets for analysing crossplatform user behaviours, especially across GitHub and Stack Overflow. Users on GitHub and Stack Overflow are identifiable by equivalences of email addresses. In order to increase the number of identifiable users on these datasets, this paper retrieves potentially identifiable users between GitHub and Stack Overflow not relying only on email addresses. This paper employs a classification-based link prediction, which design the user identification problem as a link prediction problem on the bipartite graph consisting of users of GitHub and those of Stack Overflow. With the identification method, this paper generates a probabilistic dataset containing pairs of users with probabilities (or confidences). This paper, as well, publishes the identification tool in order to enable further data generation on appearing datasets of GitHub, Stack Overflow and others. The generated dataset and tool are highly helpful to accelerate researches on mining software repositories.

    DOI: 10.18293/SEKE2017-109

    Scopus

  71. FORK: Feedback-Aware ObjectRank-Based Keyword Search over Linked Data Reviewed

    Takahiro Komamizu, Sayami Okumura, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 10648   page: 58 - 70   2017

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Springer Verlag  

    Ranking quality for keyword search over Linked Data (LD) is crucial when users look for entities from LD, since datasets in LD have complicated structures as well as much contents. This paper proposes a keyword search method, FORK, which ranks entities in LD by ObjectRank, a well-known link-structure analysis algorithm that can deal with different types of nodes and edges. The first attempt of applying ObjectRank to LD search reveals that ObjectRank with inappropriate settings gives worse ranking results than PageRank which is equivalent to ObjectRank with all the same authority transfer weights. Therefore, deriving appropriate authority transfer weights is the most important issue for encouraging ObjectRank in LD search. FORK involves a relevance feedback algorithm to modify the authority transfer weights according with users’ relevance judgements for ranking results. The experimental evaluation of ranking qualities using an entity search benchmark showcases the effectiveness of FORK, and it proves ObjectRank is more feasible raking method for LD search than PageRank and other comparative baselines including information retrieval techniques and graph analytic methods.

    DOI: 10.1007/978-3-319-70145-5_5

    Scopus

  72. Towards Real-time Analysis of Smart City Data: A Case Study on City Facility Utilizations Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Salman Ahmed Shaikh, Hiroaki Shiokawa, Hiroyuki Kitagawa

    Proc. the 14th IEEE International Conference on Smart City (SmartCity 2016)     page: 1357-1364   2016.12

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    Authorship:Lead author   Language:English  

    DOI: 10.1109/HPCC-SmartCity-DSS.2016.0192

  73. Interleaving Clustering of Classes and Properties for Disambiguating Linked Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 18th International Conference on Asia-Pacific Digital Libraries (ICADL 2016)     2016.12

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    Authorship:Lead author   Language:English  

    DOI: 251-256

  74. Visual Spatial-OLAP for Vehicle Recorder Data on Micro-sized Electric Vehicles Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 20th International Database Engineering & Applications Symposium (IDEAS 2016)     page: 358-363   2016.7

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    Authorship:Lead author   Language:English  

    DOI: 10.1145/2938503.2938532

  75. H-SPOOL: A SPARQL-based ETL Framework for OLAP over Linked Data with Dimension Hierarchy Extraction Invited Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

      Vol. 12 ( 3 ) page: 359-378   2016.6

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1108/IJWIS-03-2016-0014

  76. SPOOL: A SPARQL-based ETL Framework for OLAP over Linked Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2015)   ( 49 ) page: 1-10   2015.12

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    Authorship:Lead author   Language:English  

    DOI: 10.1145/2837185.2837230

  77. Facet-value Extraction Scheme from Textual Contents in XML Data Invited Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

      Vol. 11 ( 3 ) page: 270-290   2015.6

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1108/IJWIS-04-2015-0012

  78. Extracting Facets from Textual Contents for Faceted Search over XML Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2014)     page: 420-429   2014.12

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    Authorship:Lead author   Language:English  

    DOI: 10.1145/2684200.2684294

  79. Frequent-Pattern based Facet Extraction from Graph Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 17th International Conference on Network-Based Information Systems (NBiS 2014)     page: 318-323   2014.9

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    Authorship:Lead author   Language:English  

    DOI: 10.1109/NBiS.2014.77

  80. A Scheme of Automated Object and Facet Extraction for Faceted Search over XML Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 18th International Database Engineering & Applications Symposium (IDEAS 2014)     page: 338-341   2014.7

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    Authorship:Lead author   Language:English  

    DOI: 10.1145/2628194.2628241

  81. A Scheme of Fragment-Based Faceted Image Search Reviewed

    Takahiro Komamizu, Mariko Kamie, Kazuhiro Fukui, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 23rd International Conference on Database and Expert Systems Applications (DEXA 2012)     page: 450-457   2012.9

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    Authorship:Lead author   Language:English  

    DOI: 10.1007/978-3-642-32597-7_40

  82. Faceted Navigation Framework for XML Data Invited Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

      Vol. 8 ( 4 ) page: 348-370   2012.8

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1108/17440081211282865

  83. A Framework of Faceted Navigation for XML Data Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 13th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2011)     page: 28-35   2011.12

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    Authorship:Lead author   Language:English  

    DOI: 10.1145/2095536.2095544

▼display all

MISC 6

  1. Requirements for Real-World Data Practicum Programs and their Practices

    MATSUBARA, Shigeki, NAKAIWA, Hiromi, KOMAIMIZU, Takahiro, SUZUKI, Yu, IDE, Ichiro, NISHIMURA, Norihiro, HAYAMIZU, Satoru, TAKEDA, Kazuya

      Vol. 122 ( 431 ) page: 117 - 127   2023.3

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    Language:Japanese  

    IEICE Technical Report ET2022-78

  2. 産学コンソーシアムによるデータサイエンス人材育成の実践

    松原, 茂樹, 中岩, 浩巳, 駒水, 孝裕, 鈴木, 優, 井手, 一郎, 西村, 訓弘, 速水, 悟, 武田, 一哉

    情報処理学会研究報告コンピュータと教育(CE)   Vol. 2022-CE-167 ( 16 ) page: 1 - 6   2022.11

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    Language:Japanese   Publisher:一般社団法人情報処理学会  

    データサイエンス人材育成を産学連携で実践する教育プログラムの設計と運用について述べる。本プログラムは、大学院生および社会人を対象とし、データサイエンティストに必要な能力である「実世界データ知識」「ツール活用スキル」「異分野協業マインド」を涵養することを目的とする。企業や地方公共団体が提供するデータを用いてグループワークにより課題を解決する「実世界データ演習」を中核とするプログラムを設計した。複数の大学が産業界と連携して教育するためのガイドラインを整備することで、課題、データ、ツール、メンタなどの教育資源を大学間で共用することを可能としている。本プログラムを開講した2019年度から2021年度までの3年間で165名の修了生を輩出している。
    コンピュータと教育(CE)研究発表会(2022年12月3日(土)~4日(日) 福岡工業大学 and オンライン)

  3. Towards Mobility-related Law Search by Utilizing Relationship between Laws

    KOMAMIZU Takahiro, TOYAMA Katsuhiko, KAWAGUCHI Nobuo, SANO Tomoya

    JSAI Technical Report, Type 2 SIG   Vol. 2022 ( SWO-057 ) page: 04   2022.8

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    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

    DOI: 10.11517/jsaisigtwo.2022.swo-057_04

    CiNii Research

  4. The Web Conference 2020 参加報告

    駒水, 孝裕

    情報処理   Vol. 61 ( 10 ) page: 1078 - 1079   2020.9

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    Language:Japanese  

    CiNii Books

  5. Identifying Legal Entities in DBpedia for Statute History LOD

    KOMAMIZU Takahiro, OGAWA Yasuhiro, TOYAMA Katsuhiko

    JSAI Technical Report, Type 2 SIG   Vol. 2020 ( SWO-051 ) page: 06   2020.7

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    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

    DOI: 10.11517/jsaisigtwo.2020.swo-051_06

    CiNii Research

  6. Japanese Legal Term Correction using BERT Pretrained Model

    YAMAKOSHI Takahiro, KOMAMIZU Takahiro, OGAWA Yasuhiro, TOYAMA Katsuhiko

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2020 ( 0 ) page: 4P3OS805 - 4P3OS805   2020

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    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

    Legal documents contain legal terms that have similar meaning or pronunciation each other. Japanese legislation defines their usage on the basis of traditional customs and rules. In accordance with the definition, we need to use these legal terms properly and strictly in a statute. We are also encouraged to follow the definition in writing broad-sense legal documents, such as contracts and terms of use. To assist in writing legal documents, we propose a method that locates inappropriate legal terms in Japanese statutory sentences and suggests corrections. We solve this task with a classifier by regarding the task as a sentence completion test. Our classifier is based on a pretrained BERT model trained by using a large amount of general sentences. To raise performance, we apply three training techniques: domain adaptation, undersampling, classifier unification. Our experiments show that our classifier achieved better performance than Random Forest-based ones and language model-based ones.

    DOI: 10.11517/pjsai.jsai2020.0_4p3os805

    CiNii Research

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Presentations 32

  1. 法令沿革オントロジーの設計

    内田 勇志, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    第47回人工知能学会セマンティックウェブとオントロジー(SWO)研究会 

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    Event date: 2019.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  2. グラフ構造を利用したエンティティ検索

    駒水 孝裕

    第11回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2019.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  3. 部分構造を用いた類似例規の検索

    藤岡 和弥, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    第11回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2019.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  4. nagoy Team’s Summarization System at the NTCIR-14 QA Lab-PoliInfo

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    Event date: 2019

    Language:English   Presentation type:Oral presentation (general)  

    DOI: 10.1007/978-3-030-36805-0_9

    Scopus

  5. 並列構造の分割による法令文の読解性向上

    青山 恵子, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  6. ランダムフォレストによる法令用語の校正

    山腰 貴大, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  7. ニューラルモデルと翻訳メモリを併用した機械翻訳

    重野 泰和, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  8. 分類器を用いた法令要約に利用する法令文の自動抽出

    佐藤 充晃, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  9. 単語の分散表現を用いた法令用語間の関係の獲得

    植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  10. Zipf の法則は例規文の出現数においても成立する

    藤岡 和弥, 駒水 孝裕, 小川 泰弘, 外山 勝彦

    平成30年度 電気・電子・情報関係学会 東海支部連合大会 

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    Event date: 2018.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  11. グラフ集約に基づくRDFデータに対するOLAP分析

    仁木 美来, 天笠 俊之, 駒水 孝裕, 北川 博之

    情報処理学会第80回全国大会 

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    Event date: 2018.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  12. ノードがテキスト情報を持つ動的ネットワークにおけるノードと単語の分散表現学習

    伊藤 寛祥, 駒水 孝裕, 天笠 俊之, 北川 博之

    第10回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2018.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  13. ゴミ減量G1グランプリ Invited

    駒水 孝裕

    第1回 地域IoTと情報力シンポジウム 

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    Event date: 2017.4

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  14. GitHubとStack Overflowにおけるユーザ行動の統一的な分析

    永野 真知, 早瀬 康裕, 駒水 孝裕, 北川 博之

    情報処理学会第79回全国大会 

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    Event date: 2017.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  15. ノードが複数の属性を持つグラフにおけるコミュニティ検出

    伊藤 寛祥, 駒水 孝裕, 天笠 俊之, 北川 博之

    第9回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2017.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  16. XMLデータに対するファセット検索のためのファセット抽出の自動化

    駒水 孝裕, 天笠 俊之, 北川 博之

    第13回情報科学技術フォーラム 

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    Event date: 2014.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  17. グラフデータに対するファセット探索のための頻出パターンを利用したオブジェクト抽出手法

    駒水 孝裕, 天笠 俊之, 北川 博之

    第4回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2012.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  18. データ工学分野における技術と研究 Invited

    駒水 孝裕

    科目「ICT活用」 

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    Event date: 2012.2

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

    Country:Japan  

  19. ソフトウェア部品検索に適したファセット探索の一考察

    駒水 孝裕, 早瀬 康裕, 北川 博之

    ソフトウェアサイエンス研究会 

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    Event date: 2011.10

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  20. FACTUS: Faceted Twitter User Search Using Twitter Lists International conference

    Takahiro Komamizu, Yuto Yamaguchi, Toshiyuki Amagasa, Hiroyuki Kitagawa

    Proc. the 12th International Conference on Web Information System Engineering (WISE 2011) 

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    Event date: 2011.10

    Language:English   Presentation type:Poster presentation  

    Country:Australia  

  21. XMLデータに対するファセット検索のユーザビリティ評価

    駒水 孝裕, 天笠 俊之, 北川 博之

    情報処理学会第73回全国大会 

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    Event date: 2011.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  22. キーワード検索が可能なXMLデータに対するファセット探索

    駒水 孝裕, 天笠 俊之, 北川 博之

    第3回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2011.2 - 2011.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  23. 異種XMLデータに対するファセット検索システムの性能評価

    駒水 孝裕, 天笠 俊之, 北川 博之

    情報処理学会第72回全国大会 

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    Event date: 2010.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  24. 異種XMLデータに対するファセット検索における多様な検索

    駒水 孝裕, 天笠 俊之, 北川 博之

    第2回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2010.2 - 2010.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  25. 異種XMLデータに対するファセット検索手法の提案

    駒水 孝裕, 天笠 俊之, 北川 博之

    デジタルドキュメント研究会 

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    Event date: 2009.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  26. XMLデータに対するファセットナビゲーションのためのフレームワークFoXの提案

    駒水 孝裕, 天笠 俊之, 北川 博之

    第1回データ工学と情報マネジメントに関するフォーラム 

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    Event date: 2009.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  27. Analyzing Japanese law history through modeling multi-versioned entity

    Komamizu T.

    CEUR Workshop Proceedings  2019  CEUR Workshop Proceedings

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  28. Thai legal term correction using random forests with outside-the-sentence features

    Yamakoshi T.

    Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019  2019  Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019

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  29. SPARQL with XQuery-based filtering

    Komamizu T.

    CEUR Workshop Proceedings  2020  CEUR Workshop Proceedings

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  30. Muensemble: Multi-ratio undersampling-based ensemble framework for imbalanced data

    Komamizu T.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  2020  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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  31. Learning Interpretable Entity Representation in Linked Data

    Komamizu T.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  2018  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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  32. Exploring Relevant Parts Between Legal Documents Using Substructure Matching

    Komamizu T.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  2020  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Research Project for Joint Research, Competitive Funding, etc. 1

  1. 公益財団法人 人工知能研究振興財団 研究助成

    2019.1 - 2020.9

KAKENHI (Grants-in-Aid for Scientific Research) 7

  1. オンデマンド型仮想六法の構成方法の研究開発

    Grant number:23K18507  2023.6 - 2025.3

    日本学術振興会  科学研究費助成事業  挑戦的研究(萌芽)

    駒水 孝裕, 外山 勝彦, 佐野 智也

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    Authorship:Principal investigator 

    Grant amount:\6370000 ( Direct Cost: \4900000 、 Indirect Cost:\1470000 )

    法令は社会を安全に運用するために必要なルールを定める.しかし,規定内容が複数の法令にまたがっていることなどの理由から,ある事物に関する規定を網羅的に把握することは容易でない.本提案では,オンデマンド型仮想六法の構成方法を開発する.任意のトピックに関する法令集の編纂は人的労力がかかるため,特化した仮想六法の構築を容易にすることで,特定の事物に関する規定が分散した現状を打開する.これを実現するために,本提案では,法情報管理の新しい方法論として,概念指向法情報管理 COLIM (Concept-Oriented Legal Information Management) を提案する.

  2. Quality-Assured End-to-End Big Data Approximation Processing

    Grant number:22H03594  2022.4 - 2026.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  3. Analysis of the visual characteristics of language information and its application to multimedia integrated processing

    Grant number:22H03612  2022.4 - 2026.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  4. Digital transformation and dissemination of legal information in local governments

    Grant number:22H03901  2022.4 - 2026.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  5. Management and Integration for Linked Open Multimedia Data

    Grant number:21H03555  2021.4 - 2025.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Principal investigator 

    Grant amount:\17160000 ( Direct Cost: \13200000 、 Indirect Cost:\3960000 )

  6. 大規模データ分析のための多視点分析管理システムの研究開発

    2018.4 - 2021.3

    科学研究費補助金 

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    Authorship:Principal investigator 

  7. Multi-dimensional Analysis and Management for Large and Various Data

    Grant number:18K18056  2018.4 - 2021.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Early-Career Scientists

    Komamizu Takahiro

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    Authorship:Principal investigator 

    Grant amount:\4160000 ( Direct Cost: \3200000 、 Indirect Cost:\960000 )

    In the digital transformation era, utilizing open data for various applications is an important issue. In this research, two techniques are developed; one is efficient search entities from graph-structured data consisting of relationships between data, and the other is an integration technique for data published by different organizations. On the basis of these techniques, efficient extracting data of interest becomes possible, and precise data analysis using data of multiple granularity.

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Teaching Experience (On-campus) 3

  1. 情報工学実験

    2018

  2. 数理科学基礎演習

    2018

  3. データ処理ツール演習

    2018