Ryo Nishimura


2010

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A Context Sensitive Variant Dictionary for Supporting Variant Selection
Aya Nishikawa | Ryo Nishimura | Yasuhiko Watanabe | Yoshihiro Okada
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In Japanese, there are a large number of notational variants of words. This is because Japanese words are written in three kinds of characters: kanji (Chinese) characters, hiragara letters, and katakana letters. Japanese students study basic rules of Japanese writing in school for many years. However, it is difficult to learn which variant is suitable for a certain context in official, business, and technical documents because the rules have many exceptions. Previous Japanese writing support systems were not concerned with them sufficiently. This is because their main purposes were misspelling detection. Students often use variants which are not misspelling but unsuitable for the contexts in official, business, and technical documents. To solve this problem, we developed a context sensitive variant dictionary. A writing support system based on the context sensitive variant dictionary detects unsuitable variants for the contexts in students' reports and shows suitable ones to the students. In this study, we first show how to develop a context sensitive variant dictionary by which our system determines which variant is suitable for a context in official, business, and technical documents. Finally, we conducted a control experiment and show the effectiveness of our dictionary.

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Detection of submitters suspected of pretending to be someone else in a community site
Naoki Ishikawa | Ryo Nishimura | Yasuhiko Watanabe | Yoshihiro Okada | Masaki Murata
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

One of the essential factors in community sites is anonymous submission. This is because anonymity gives users chances to submit messages (questions, problems, answers, opinions, etc.) without regard to shame and reputation. However, some users abuse the anonymity and disrupt communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect submitters suspected of pretending to be someone else to manipulate communications in a community site by using machine learning techniques. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.

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Detection of Users Suspected of Pretending to Be Other Users in a Community Site by Using Messages Submitted to Non-Target Categories
Naoki Ishikawa | Ryo Nishimura | Yasuhiko Watanabe | Masaki Murata | Yoshihiro Okada
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

2008

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Confirmed Language Resource for Answering How Type Questions Developed by Using Mails Posted to a Mailing List
Ryo Nishimura | Yasuhiko Watanabe | Yoshihiro Okada
Proceedings of the 6th Workshop on Asian Language Resources

2005

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Confirmed Knowledge Acquisition Using Mails Posted to a Mailing List
Yasuhiko Watanabe | Ryo Nishimura | Yoshihiro Okada
Second International Joint Conference on Natural Language Processing: Full Papers

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A Question Answer System Based on Confirmed Knowledge Developed by Using Mails Posted to a Mailing List
Ryo Nishimura | Yasuhiko Watanabe | Yoshihiro Okada
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts