Faculty Profiles - MATSUBARA, Shigeki

Papers - MATSUBARA, Shigeki

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否定理解能力を評価するための日本語言語推論データセットJNLI-Neg Reviewed

吉田朝飛，加藤芳秀，小川泰弘，松原茂樹

情報処理学会論文誌 2025.12

Where Did the Research Data Originate? Acquiring Provenance of Research Data from Scholarly Papers Reviewed

Koshi Motegi, Koichiro Ito, Shigeki Matsubara

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025.12

Dataset Similarity Estimation Using LLM-based Metadata Embeddings Reviewed

Koichiro Ito, Shigeki Matsubara

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025.12

大学における研究データマネジメント推進のための生成AI チャットボットの開発 Reviewed

和田和浩，松原茂樹

学術情報処理研究 2025.11

Classification of URL Citations on Scholarly Papers Using Intermediate Task Training Reviewed Open Access

WADA Kazuhiro, TSUNOKAKE Masaya, MATSUBARA Shigeki

IEICE Transactions on Information and Systems Vol. advpub ( 0 ) 2025.11

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

Citations using URL (URL citations) that appear in scholarly papers can be used as an information source for the research resource search engines. In particular, the information about the types of cited resources and reasons for their citation is crucial to describe the resources and their relations in the search services. To obtain this information, previous studies proposed some methods for classifying URL citations. However, their methods trained the model using a simple fine-tuning strategy and exhibited insufficient performance. We propose a classification method using a novel intermediate task. Our method trains the model on our intermediate task of identifying whether sample pairs belong to the same class before being fine-tuned on the target task. In the experiment, our method outperformed previous methods using the simple fine-tuning strategy with higher macro F-scores for different model sizes and architectures. Our analysis results indicate that the model learns the class boundaries of the target task by training our intermediate task. Our intermediate task also demonstrated higher performance and computational efficiency than an alternative intermediate task using triplet loss. Finally, we applied our method to other text classification tasks and confirmed the effectiveness when a simple fine-tuning strategy does not stably work.

DOI： 10.1587/transinf.2024edp7149

Open Access

CiNii Research

Negation Scope Conversion for Unifying Negation-Annotated Datasets Reviewed Open Access

Yoshida Asahi, Kato Yoshihide, Matsubara Shigeki

Journal of Natural Language Processing Vol. 32 ( 1 ) page： 300 - 329 2025.3

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：The Association for Natural Language Processing

Negation scope resolution is a technique that identifies the part of a sentence affected by the negation cue. The three major corpora used for it, the BioScope corpus, the SFU review corpus, and the Sherlock dataset, have different annotation schemes for negation scope. Due to the different annotations, it is difficult to use the three corpora together in the study of negation scope resolution. To address this issue by merging the corpora into a unified dataset based on a common annotation scheme, we propose a method for automatically converting the scopes of BioScope and SFU to those of Sherlock. We conducted an experiment to evaluate the accuracy of our method using a dataset obtained by manually annotating the negation scopes to a tiny portion of BioScope and SFU, verifying that our method can convert the scopes with high accuracy. In addition, we conducted another experiment to verify the effectiveness of our method from a pragmatic perspective, where we fine-tuned PLM-based negation scope resolution models using the unified dataset obtained by our method. The results demonstrated that the performances of the models increase when fine-tuned on the unified dataset, unlike the simply combined one, which supports the effectiveness of our method.

DOI： 10.5715/jnlp.32.300

Open Access

CiNii Research

Human Performance in Incremental Dependency Parsing: Dependency Structure Annotations and their Analyses Reviewed

Hiroki Unno, Tomohiro Ohno, Koichiro Ito, Shigeki Matsubara

Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC-2024) 2024.12

Citation-Worthy Detection of URL Citations in Scholarly Papers Reviewed Open Access

Kazuhiro Wada, Masaya Tsunokake, Shigeki Matsubara

Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries (JCDL-2024) ( 28 ) page： 1 - 5 2024.12

Capabilities and Challenges of LLMs in Metadata Extraction from Scholarly Papers Reviewed

Watanabe Y., Ito K., Matsubara S.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15493 page： 280 - 287 2024.12

Estimation of Relevance Between Datasets for Enhancing Accessibility of Research Artifacts Reviewed

Ito K., Matsubara S.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15494 page： 97 - 103 2024.12

The Power of URLs in Scholarly Papers: Their Role as Metadata Sources for Data Repositories Reviewed

Wada K., Matsubara S.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15494 page： 85 - 92 2024.12

Enhancing accessibility of research data through metadata conversion and registration in institutional repositories: Practices in the field of Solar-Terrestrial Physics Invited

NOSÉ Masahito, SHINBORI Atsuki, MIYOSHI Yoshizumi, HORI Tomoaki, OOHIRA Tsukasa, OKAMOTO Maiko, NAOE Chizuko, GAKIYA Rui, TANAKA Sachie, SAGARA Takeshi, AOKI Takaaki, MATSUBARA Shigeki, TAKAHASHI Ichiro, HAYASHI Hidekazu, YAMADA Kazunari, MINAMIYAMA Yasuyuki, TANAKA Yoshimasa, ABE Shuji, UENO Satoru, IMAJO Shun, SAITO Yasuo, ASHIKITA Takuya, HORI Yuko, SHIMIZU Toshiyuki, OKAMURA Nanako, HIRANO Kaoru

The Journal of Information Science and Technology Association Vol. 74 ( 11 ) page： 487 - 493 2024.11

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Information Science and Technology Association, Japan

The “Inter-university Upper atmosphere Global Observation NETwork (IUGONET)” project has developed a metadata database for ground-based observation data in Solar-Terrestrial Physics. In order to make this metadata database accessible to a wide range of research communities and the general public, we converted metadata in different metadata schema and registered them in academic institutional repositories. As a result, through regular harvesting between databases, the metadata has also been registered in the Institutional Repositories Database (IRDB), the Data Catalog Cross-Search System, and Google’s Dataset Search, significantly enhancing the visibility and accessibility of research data. Furthermore, effective metadata management was implemented in response to research metadata maintenance requests from policy-making bodies.

DOI： 10.18919/jkg.74.11_487

CiNii Research

Utilization of Text Data for Response Timing Detection in Attentive Listening Reviewed

Yu Watanabe, Koichiro Ito, Shigeki Matsubara

Proceedings of Interspeech-2024 page： 3565 - 3569 2024.9

Estimating Metadata of Research Artifacts to Enhance their Findability Reviewed

Koichiro Ito, Shigeki Matsubara

Proceedings of 2024 IEEE 20th International Conference on e-Science (e-Science) page： 1 - 2 2024.9

Enhancing Findability and Searchability of Research Data: Metadata Conversion and Registration in Institutional Repositories Reviewed Open Access

Nosé M., Shinbori A., Miyoshi Y., Hori T., Ohira T., Hashiba J., Naoe C., Gakiya R., Okamoto M., Sagara T., Aoki T., Matsubara S., Takahashi I., Hayashi H., Yamada K., Minamiyama Y., Tanaka Y., Abe S., Ueno S., Imajo S., Saito Y., Ashikita T., Hori Y., Shimizu T., Okamura N., Hirano K., Bargatze L.

Data Science Journal Vol. 23 2024.7

On an Intermediate Task for Classifying URL Citations on Scholarly Papers Reviewed

Kazuhiro Wada, Masaya Tsunokake, Shigeki Matsubara

Proceeding of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING2024) 2024.5

Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset Reviewed

Asahi Yoshida, Yoshihide Kato, Shigeki Matsubara

Proceeding of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING2024) 2024.5

Responsive Utterance Generation to Show Disagreement to Narrative in Attentive Listening Reviewed

Ito Koichiro, Murata Masaki, Ohno Tomohiro, Matsubara Shigeki

Journal of Natural Language Processing Vol. 31 ( 1 ) page： 212 - 249 2024.3

Authorship：Last author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Association for Natural Language Processing

Conversational agents, such as communication robots, are expected to play a role in listening to narratives. To recognize these robots as the listeners, it is essential for them to have a function that indicates their attentive listening to a narrative. The basic response strategy in attentive listening involves generating responsive utterances that show acceptance. However, the narratives occasionally contain self-deprecation or modesty. In such cases, the listener must be able to produce a response that shows disagreement with the narrator's utterance, that is, a disagreement response. This study demonstrates the feasibility of generating appropriate disagreement responses. First, we define a method to tag the timing and expression of the disagreement response to narrative data in a non-real-time environment, and verify that the method enables to construct a corpus to which the disagreement response timing and response expression are comprehensively and stably assigned, respectively. Next, we implement detection and classification methods for disagreement response timing and expressions, respectively, based on a pre-trained transformer-based model and verify the feasibility of generating disagreement responses using the response corpus through experiments.

DOI： 10.5715/jnlp.31.212

CiNii Research

Re-Evaluating Syntax-Based Negation Scope Resolution Open Access

YOSHIDA Asahi, KATO Yoshihide, MATSUBARA Shigeki

IEICE Transactions on Information and Systems Vol. E107.D ( 1 ) page： 165 - 168 2024.1

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

Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous researches, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.

DOI： 10.1587/transinf.2023edl8050

Paper Recommendation Using Citation Contexts in Scholarly Documents Reviewed

Tomoki Ikoma, Shigeki Matsubara

Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC 37) 2023.12

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Papers - MATSUBARA, Shigeki