@techreport{oai:ipsj.ixsq.nii.ac.jp:00048166,
 author = {市村, 由美 and 齋藤, 佳美 and 酒井, 哲也 and 國分智晴 and 小山, 誠 and Yumi, Ichimura and Yoshimi, Saito and Tetsuya, Sakai and Tomoharu, Kokubu and Makoto, Koyama},
 issue = {47(2004-NL-161)},
 month = {May},
 note = {日本語固有表現抽出システムを知識抽出と質問解析に利用した質問応答システムASKMiを開発した．このシステムを用いて，(1) 質問応答の性能と固有表現抽出の性能の関係，(2) 質問応答の性能と固有表現体系の粒度の関係，について考察を行う．毎日新聞98?99年版の2年分の新聞記事とQAC1のテストコレクションを用いた実験により，次のような知見が得られたことを示す．(1)固有表現抽出の再現率や適合率が向上すると，質問応答の性能も向上する．しかし，再現率が十分に高くないときに適合率だけを向上させると，質問応答の性能低下を招く．(2)固有表現体系は，全体としては細分化する方が質問応答の性能が良いが，適切な細分化の粒度は，固有表現クラスによって異なる．, We have developed a question answering system called ASKMi (Answer Seeker/Knowledge Miner) which utilizes a Japanese named entity extractor as a knowledge miner and a question analyzer. With this system, we study (i)the relationship between the performance of question answering and that of named entity extraction, and (ii)the relationship between the performance of question answering and the granularity of named entity taxonomy. Through experiments with Mainichi Daily News ('98-'99) and QAC1 test collection, we will show that (i)the performance of question answering increases with increasing recall and precision of named entity extraction, but high precision without sufficiently high recall leads to worse performance of question answering, and (ii)fine subdivision of named entity taxonomy leads to better performance of question answering, while the appropriate level of granularity varies across named entity classes.},
 title = {質問応答と，日本語固有表現抽出および固有表現体系の関係についての考察},
 year = {2004}
}