Updated on 2023/03/07

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 1

  1. University of Tsukuba   Center for Computational Sciences   Researcher

    2015.4 - 2018.1

      More details

    Country:Japan

Education 3

  1. University of Tsukuba

    2011.4 - 2015.3

      More details

    Country: Japan

  2. University of Tsukuba

    2009.4 - 2011.3

      More details

    Country: Japan

  3. University of Tsukuba

    2005.4 - 2009.3

      More details

    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

▼display all

Committee Memberships 18

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

    2020.11 - 2021.1   

  2. 第18回情報学ワークショップ   プログラム委員  

    2020.9 - 2020.11   

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

    2020.6   

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

    2020.4 - 2020.10   

  5.   PC member  

    2019.9 - 2020.2   

  6. 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   

  7. 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   

  8.   PC member  

    2019.4 - 2019.11   

  9. 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   

  10.   PC member  

    2018.12 - 2019.2   

  11.   PC member  

    2018.10 - 2019.7   

  12. 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   

  13. 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   

  14. 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   

  15. 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   

  16. 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   

  17. 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   

  18. DBSJ電子広報編集委員会   編集委員  

    2015.8   

▼display all

Awards 13

  1. 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

     More details

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

  2. 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

     More details

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

  3. 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

     More details

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

  4. 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

     More details

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

  5. 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

     More details

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

  6. 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

     More details

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

  7. 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

     More details

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

  8. JURIX 2018 Best paper award

    2018.12   Japanese Legal Term Correction using Random Forests

     More details

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

  9. 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

     More details

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

  10. iiWAS 2015 Best paper award

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

     More details

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

  11. 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  

     More details

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

  12. 山下記念研究賞

    2011.3   情報処理学会  

     More details

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

  13. 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  

     More details

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

▼display all

 

Papers 60

  1. 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

     More details

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

    DOI: 10.1145/3568562.3568635

    Scopus

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

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

    2022 18TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS 2022)     2022

     More details

    Publisher:AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance  

    We propose a method to detect birds in a 3D environment referring to both audio information observed from a microphone array and visual information observed from a panorama camera. In general, in panorama images, birds appear relatively too small to be detected accurately even with the state-of-the-art deep learning models. Thus, the proposed method takes a two step approach where the birds are first roughly located referring to audio information by Sound Source Localization (SSL), and then image detection is applied within its vicinity. Through evaluation on a dataset annotated with bounding boxes surrounding the birds, we show that the proposed method improves detection performance of birds that appear in relatively small sizes in the image, in both accuracy and processing speed.

    DOI: 10.1109/AVSS56176.2022.9959510

    Web of Science

    Scopus

  3. Action Semantic Alignment for Image Captioning

    Huo D., Kastner M.A., Komamizu T., Ide I.

    Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022     page: 194 - 197   2022

     More details

    Publisher:Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022  

    Image captioning is one of the main goals in vision and language processing, which aims to generate proper descriptions of images. Recently, the attention mechanisms became crucial in captioning tasks, as they can capture global dependencies between modalities. Moreover, some works have used objects detected from the input image as anchor points, so called object tags, to ease such alignments resulting in good performance for this task. In this paper, we newly introduce action information as a prior to further improve this, by adding action tags for training. The action tags can learn alignment at action semantic level and catch the previously ignored dimension of action, that could be very important in image captioning. We found that training with action tags can be used to describe images in a dynamic style. Furthermore, we found it can actually lead to a significant improvement compared with other methods in captioning performance measured by common metrics.

    DOI: 10.1109/MIPR54900.2022.00041

    Scopus

  4. 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

     More details

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

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

    Web of Science

    Scopus

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

    Kato H., Hirayama T., Doman K., Ide I., Kawanishi Y., Komamizu T., Deguchi D., Murase H.

    Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022     page: 240 - 245   2022

     More details

    Publisher:Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022  

    Gait is one of the most familiar action for us, that is why we can distinguish slight difference of human gaits and perceive their impressions. However, the relationship has been never explored because of the absence of intuitive labels for the slight differences. In this paper, to solve this problem, we propose a intuitive gait model using Japanese mimetic-words. A mimetic-word has sound-symbolism, which means that there is an association between linguistic sounds and sensory experiences, and the phonemes of a mimetic-word is strongly related to the visual sensation. Thanks to the sound-symbolism, Japanese mimetic-words have a possibility of modeling gaits intuitively. Thus, we have previously proposed a method which describes gait with a mimetic-word. In this paper, in the opposite direction, we propose a method which generates gait from a mimetic-word, and confirm the effectiveness of the proposed intuitive gait model which consists of the phonetic-vector through evaluations of both the generation task and the description task.

    DOI: 10.1109/MIPR54900.2022.00050

    Scopus

  6. 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

     More details

  7. 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

     More details

    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

  8. 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

     More details

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

    DOI: 10.1109/MIPR51284.2021.00018

    Scopus

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

  9. 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

     More details

    Publisher:WAT 2021 - 8th Workshop on Asian Translation, Proceedings of the Workshop  

    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.

    Scopus

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

    Takahiro Komamizu

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

     More details

    Publishing type:Research paper (scientific journal)  

    DOI: 10.26421/JDI2.4-5

  11. 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

     More details

    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s10115-020-01445-4

    Web of Science

    Scopus

  12. 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

     More details

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

    <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

    Scopus

    CiNii Research

  13. 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

     More details

    Language:English   Publisher:The Japanese Society for Artificial Intelligence  

    <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

    Scopus

    CiNii Research

  14. 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

     More details

    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

  15. 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

     More details

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

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

    Web of Science

    Scopus

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

  16. SPARQL with XQuery-based Filtering.

    Takahiro Komamizu

    CoRR   Vol. abs/2009.06194   2020

     More details

    Publishing type:Research paper (scientific journal)  

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

  17. SPARQL with XQuery-based filtering

    Komamizu T.

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

     More details

    Publisher:CEUR Workshop Proceedings  

    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.

    Scopus

  18. 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

     More details

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

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

    Web of Science

    Scopus

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

  19. 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

     More details

    Language:English  

  20. 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

     More details

    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

  21. 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

     More details

    Authorship:Lead author   Language:English  

  22. 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

     More details

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

  23. 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

     More details

    Authorship:Lead author   Language:English  

  24. 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

     More details

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

  25. 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

     More details

    Language:English  

  26. 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

     More details

    Language:Japanese  

  27. 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

     More details

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

  28. 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

     More details

    Language:English  

  29. 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

     More details

    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

  30. 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

     More details

    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

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

    Yamakoshi Takahiro, Komamizu Takahiro, Ogawa Yasuhiro, Toyama Katsuhiko

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

     More details

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

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

    CEUR Workshop Proceedings   Vol. 2599   2019

     More details

    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

  33. 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

     More details

    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

  34. 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

     More details

    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

  35. Graph Analytical Re-ranking for Entity Search

    Komamizu T.

    CEUR Workshop Proceedings   Vol. 2482   2019

     More details

    Publisher:CEUR Workshop Proceedings  

    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

  36. 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

     More details

    Language:English  

  37. 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

     More details

    Authorship:Lead author   Language:English  

  38. 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

     More details

    Language:English  

    DOI: 2-9

  39. 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

     More details

    Language:English  

    DOI: 334-339

  40. 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

     More details

    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

  41. 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

     More details

    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

  42. 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

     More details

  43. Learning Interpretable Entity Representation in Linked Data.

    Takahiro Komamizu

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

     More details

    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

  44. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 284-288

  45. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 35-42

  46. 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

     More details

    Language:Japanese  

    DOI: 138-145

  47. 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

     More details

    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

  48. 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

     More details

    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

  49. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1109/HPCC-SmartCity-DSS.2016.0192

  50. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 251-256

  51. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1145/2938503.2938532

  52. 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

     More details

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

    DOI: 10.1108/IJWIS-03-2016-0014

  53. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1145/2837185.2837230

  54. 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

     More details

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

    DOI: 10.1108/IJWIS-04-2015-0012

  55. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1145/2684200.2684294

  56. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1109/NBiS.2014.77

  57. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1145/2628194.2628241

  58. 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

     More details

    Authorship:Lead author   Language:English  

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

  59. Faceted Navigation Framework for XML Data Invited Reviewed

    Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

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

     More details

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

    DOI: 10.1108/17440081211282865

  60. 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

     More details

    Authorship:Lead author   Language:English  

    DOI: 10.1145/2095536.2095544

▼display all

MISC 4

  1. 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

     More details

    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

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

    CiNii Research

  2. The Web Conference 2020 参加報告

    駒水, 孝裕

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

     More details

    Language:Japanese  

    CiNii Books

  3. 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

     More details

    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

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

    CiNii Research

  4. 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

     More details

    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

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 

     More details

    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 

     More details

    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 

     More details

    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

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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」 

     More details

    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 

     More details

    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) 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    Event date: 2009.3

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  27. 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

     More details

  28. SPARQL with XQuery-based filtering

    Komamizu T.

    CEUR Workshop Proceedings  2020  CEUR Workshop Proceedings

     More details

  29. 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)

     More details

  30. 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)

     More details

  31. 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)

     More details

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

    Komamizu T.

    CEUR Workshop Proceedings  2019  CEUR Workshop Proceedings

     More details

▼display all

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) 6

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

    Grant number:22H03612  2022.4 - 2026.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

      More details

    Authorship:Coinvestigator(s) 

  2. 地方自治体における法情報のDX化と発信

    Grant number:22H03901  2022.4 - 2026.3

    科学研究費助成事業  基盤研究(B)

    小川 泰弘, 木村 泰知, 駒水 孝裕

      More details

    Authorship:Coinvestigator(s) 

    近年進められている地方自治体のDX化においては,行政側が住民にサービスを提供するという視点で進められてきた.しかし,自治体の主役は住民であるのだから,住民側が新しいサービスを簡単に要求・実現できるようにすることが真の自治体DXだと本研究では考える.そこで,地方自治体の条例や議会会議録の情報を分かりやすく発信するシステムを開発し,それらの実現を目指す.
    具体的には,条例や会議録の要約システムや,それらの情報を有機的に結合したデータベースを開発し,それらに簡単にアクセスできる仕組みを実現する.

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

    Grant number:22H03594  2022.4 - 2026.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

      More details

    Authorship:Coinvestigator(s) 

  4. Management and Integration for Linked Open Multimedia Data

    Grant number:21H03555  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

      More details

    Authorship:Principal investigator 

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

  5. 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 

      More details

    Authorship:Principal investigator 

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

    Grant number:18K18056  2018.4 - 2021.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Early-Career Scientists

    Komamizu Takahiro

      More details

    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.

▼display all

 

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