Koichi Takeda


2020

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Development of a Medical Incident Report Corpus with Intention and Factuality Annotation
Hongkuan Zhang | Ryohei Sasano | Koichi Takeda | Zoie Shui-Yee Wong
Proceedings of the 12th Language Resources and Evaluation Conference

Medical incident reports (MIRs) are documents that record what happened in a medical incident. A typical MIR consists of two sections: a structured categorical part and an unstructured text part. Most texts in MIRs describe what medication was intended to be given and what was actually given, because what happened in an incident is largely due to discrepancies between intended and actual medications. Recognizing the intention of clinicians and the factuality of medication is essential to understand the causes of medical incidents and avoid similar incidents in the future. Therefore, we are developing an MIR corpus with annotation of intention and factuality as well as of medication entities and their relations. In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation. We then report the annotated corpus consisting of 349 Japanese medical incident reports.

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Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
Kosuke Yamada | Tsutomu Hirao | Ryohei Sasano | Koichi Takeda | Masaaki Nagata
Findings of the Association for Computational Linguistics: EMNLP 2020

Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers’ information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.

2019

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Incorporating Textual Information on User Behavior for Personality Prediction
Kosuke Yamada | Ryohei Sasano | Koichi Takeda
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.

1998

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A Method for Relating Multiple Newspaper Articles by Using Graphs, and Its Application to Webcasting
Naohiko Uramoto | Koichi Takeda
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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A Pattern-based Machine Example-Translation System Extended by based Processing
Hideo Watanabe | Koichi Takeda
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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A Method for Relating Multiple Newspaper Articles by Using Graphs, and Its Application to Webcasting
Naohiko Uramoto | Koichi Takeda
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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A Pattern-based Machine Translation System Extended by Example-based Processing
Hideo Watanabe | Koichi Takeda
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1996

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Pattern-Based Context-Free Grammars for Machine Translation
Koichi Takeda
34th Annual Meeting of the Association for Computational Linguistics

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Pattern-Based Machine Translation
Koichi Takeda
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

1994

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Tricolor DAGs for Machine Translation
Koichi Takeda
32nd Annual Meeting of the Association for Computational Linguistics

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Portable Knowledge Sources for Machine Translation
Koichi Takeda
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

1992

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Shalt2- a Symmetric Machine Translation System with Conceptual Transfer
Koichi Takeda | Naohiko Uramoto | Tetsuya Nasukawa | Taijiro Tsutsumi
COLING 1992 Volume 3: The 15th International Conference on Computational Linguistics

1986

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CRITAC - A Japanese Text Proofreading System
Koichi Takeda | Tetsunosuke Fujisaki | Emiko Suzuki
Coling 1986 Volume 1: The 11th International Conference on Computational Linguistics