Hirotoshi Taira


Citation Sentence Generation Leveraging the Content of Cited Papers
Akito Arita | Hiroaki Sugiyama | Kohji Dohsaka | Rikuto Tanaka | Hirotoshi Taira
Proceedings of the Third Workshop on Scholarly Document Processing

We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.


Task Definition and Integration For Scientific-Document Writing Support
Hiromi Narimatsu | Kohei Koyama | Kohji Dohsaka | Ryuichiro Higashinaka | Yasuhiro Minami | Hirotoshi Taira
Proceedings of the Second Workshop on Scholarly Document Processing

With the increase in the number of published academic papers, growing expectations have been placed on research related to supporting the writing process of scientific papers. Recently, research has been conducted on various tasks such as citation worthiness (judging whether a sentence requires citation), citation recommendation, and citation-text generation. However, since each task has been studied and evaluated using data that has been independently developed, it is currently impossible to verify whether such tasks can be successfully pipelined to effective use in scientific-document writing. In this paper, we first define a series of tasks related to scientific-document writing that can be pipelined. Then, we create a dataset of academic papers that can be used for the evaluation of each task as well as a series of these tasks. Finally, using the dataset, we evaluate the tasks of citation worthiness and citation recommendation as well as both of these tasks integrated. The results of our evaluations show that the proposed approach is promising.


Zero Pronoun Resolution can Improve the Quality of J-E Translation
Hirotoshi Taira | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation


Predicate Argument Structure Analysis Using Transformation Based Learning
Hirotoshi Taira | Sanae Fujita | Masaaki Nagata
Proceedings of the ACL 2010 Conference Short Papers

MSS: Investigating the Effectiveness of Domain Combinations and Topic Features for Word Sense Disambiguation
Sanae Fujita | Kevin Duh | Akinori Fujino | Hirotoshi Taira | Hiroyuki Shindo
Proceedings of the 5th International Workshop on Semantic Evaluation


BaseNP Supersense Tagging for Japanese Texts
Hirotoshi Taira | Sen Yoshida | Masaaki Nagata
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2


A Japanese Predicate Argument Structure Analysis using Decision Lists
Hirotoshi Taira | Sanae Fujita | Masaaki Nagata
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


Question Classification using HDAG Kernel
Jun Suzuki | Hirotoshi Taira | Yutaka Sasaki | Eisaku Maeda
Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering