@techreport{oai:ipsj.ixsq.nii.ac.jp:00185689,
 author = {須永, 聡 and 齋藤, 鎮成 and 宮尾, 浩 and 原田, 山人 and Satoshi, Sunaga and Tsunenari, Saitoh Hiroshi Miyao and Yamato, Harada},
 issue = {12},
 month = {Feb},
 note = {さまざまな情報検索において，関連語辞書が存在すれば，検索語の連想展開やあいまい検索などに活用できるため有効である．しかし，関連語辞書を人手により構築し更新する作業はコストがかかる．そこで，電子化された文書ファイルから言葉の共起を用いて自動的に関連語を抽出することに取り組んでいる．共起による関連語抽出では，関連性の低いまたは関連性のない言葉 （不正解関連語） が抽出される問題と抽出されない正解関連語がある，二つの問題がある．前者の問題に対しては不正解関連語の特色を見出しそれらの除外によって解決を図ることができるが，後者の問題は不正解関連語を除外する以前に，正解関連語を含むように抽出して共起語数を増やす方策が必要であり先決である．本稿では共起語の数を増やすための一手法として同義語 ・ 類義語からの共起による関連語候補抽出方法を提案する．実験による本提案手法の有効性および考察として抽出される正解関連語の範囲と同義語 ・ 類義語の意味する範囲とに同様の関係性が表れることについて述べる．, In a variety of information retrieval, if a related term dictionary exists, it is effective because it can be used for associative retrieval and fuzzy search. However, it is costly to manually construct and update a related term dictionary. Therefore, we are working on automatically extracting relevant words using co-occurrence of words from document files. In related word extraction by co-occurrence, there are two problems. These problems are that unrelated or irrelevant words (incorrect related words) are extracted and there are unextracted correct related words. For the former problem, we are working on finding the features of incorrect related words and solve them by excluding them. However, the latter problem requires a strategy to increase the number of cooccurrent words to be extracted so as to include correct related words before excluding incorrect related terms. In this paper, as a method to increase the number of co-occurring words, we propose a method for extracting related word candidates by co-occurrence from synonyms. The effectiveness of the proposed method by experiment is shown. In addition, as a consideration, we explain that similar relation appears between the range of correct related words extracted and the range meaning of synonyms.},
 title = {専門分野の関連語抽出一手法},
 year = {2018}
}