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Home > Introduction of our tenure-track faculties > Komiya Kanako

Introduction of our tenure-track faculties

Komiya Kanako

Affiliation Institute of Engineering
Division Division of Advanced Information Technology and Computer Science
Research field Natural language processing
Keyword(S) Word sense disambiguation, information extraction, domain adaptation, transfer learning
Url http://web.tuat.ac.jp/~komiya/main.html
Research experience

･Apr.2009–Mar.2010: Researcher at Precision and Intelligence Laboratory, Tokyo Institute of Technology, Japan
･Apr.2010–Mar.2014: Assistant Professor at Institute of Engineering, Tokyo University of Agriculture and Technology, Japan
･Apr.2014–Mar.2021: Lecturer at College of Engineering, Ibaraki University, Japan
･May.2018–Apr.2019: Visiting Scholar at Computer Laboratory, University of Cambridge, UK
･Apr.2021-present: Associate Professor at Institute of Engineering, Tokyo University of Agriculture and Technology, Japan
Educational background

･Mar.2005: B. S. at Institute of Engineering, Tokyo University of Agriculture and Technology, Japan
･Mar.2006: M. S. at Institute of Engineering, Tokyo University of Agriculture and Technology, Japan
･Mar.2009: Ph.D. at Institute of Engineering, Tokyo University of Agriculture and Technology, Japan
Awards

･Minoru Sasaki, Kanako Komiya, Classification of Question-Answer Pairs of QA Site Using Difference of Word Delimiter, IDR User Forum 2019, (2019.11.29). (*Yahoo! Award)
･Haruhiko Akiyama, Kanako Komiya, Yoshiyuki Kotani, Nested Monte-Carlo Search with AMAF Heuristic, 2010 International Conference on Technologies and Applications of Artificial Intelligence (TAAI 2010), pp. 172-176, Nov. (2010.11). (*Best Paper Award)
Selected papers and publications

・Kanako Komiya, Minoru Sasaki, Hiroyuki Shinnou, Manabu Okumura, Domain Adaptation using Word Embeddings for Word Sense Disambiguation, Journal of Natural Language Processing, Vol.25, No.4 pp.463-480,(2018.9).　　　　　　　　　　　　　　　　　　　
・Kanako Komiya, Masaya Suzuki, Tomoya Iwakura, Minoru Sasaki, Hiroyuki Shinnou, Comparison of Methods to Annotate Named Entity Corpora, Transactions on Asian and Low-Resource Language Information Processing, ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 17 Issue 4, Article No. 34,(2018.8)．
・Kanako Komiya, Minoru Sasaki, Hiroyuki Shinnou, Yoshiyuki Kotani, Cross-lingual Product Recommendation System Using Collaborative Filtering, Journal of Natural Language Processing, Vol. 24, No. 4，pp. 579-596, (2017.9)．
・Okumura Manabu, SHIRAI Kiyoaki, Komiya Kanako, YOKONO Hikaru, On Semeval-2010 Japanese WSD Task, Journal of Natural Language Processing, Vol.18, No.3, pp.293-307, (2011.6)

Research Description

Natural language processing is processing of languages such as English and Japanese using computers. This research area is included in research on artificial intelligence. We use machine learning, a technique where computers automatically find some rules from many examples. Our main tasks are word sense disambiguation, information extraction, sentiment analysis, recommendation systems, text classification, and so on.
In recent years, deep learning techniques have become the standard technique of artificial intelligence. In addition, we have researched on Japanese natural language processing for long years, with a special focus on properties inherent in Japanese. For example, word boundaries in Japanese are unspecific because Japanese does not have word delimiters between words and Japanese has homonyms and heteronyms because Japanese use ideograms. Moreover, we conduct research on cross-lingual text processing using English, taking into consideration these Japanese properties.
In addition, we focus on research on domain adaptation and transfer learning, which are the techniques to improve system performance when there are few data on a certain domain but there are many data on domains close to the domain. For example, we develop systems for blogs using newspapers and systems for old documents using contemporary documents.

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About TUAT's tenure-track program

This tenure track has some support systems, including start-uo grant and mentor system, I think these support systems greatly help researchers.

Future aspirations

I will do my best and enjoy my research.