{"created":"2021-03-01T06:16:57.089077+00:00","id":411,"links":{},"metadata":{"_buckets":{"deposit":"5655ec72-9aae-4857-93bf-94610b6c4440"},"_deposit":{"id":"411","owners":[],"pid":{"revision_id":0,"type":"depid","value":"411"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00000411","sets":["110:111:112","9:10:15"]},"item_2_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Estimating reliability of rules in decision lists using Bayesian learning"}]},"item_2_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2002-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"19","bibliographicPageStart":"3","bibliographicVolumeNumber":"9","bibliographic_titles":[{"bibliographic_title":"自然言語処理"}]}]},"item_2_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_2_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"統計的クラス分類器としての決定リストは，近年自然言語処理における様々な分野でその有効性を示している．決定リストを構成する上で最も重要な問題の一つは，ルールの信頼度の算出法である．決定リストを用いた多くの研究では，最尤推定法と簡単なスムージングにより信頼度を算出しているが，理論的な根拠に欠け推定精度も高くないという問題がある．そこで本論文では，ベイズ学習法を利用してルールの信頼度を算出する手法を示す．さらに，証拠の種類ごとに異なる事前分布を利用することで，より正確な信頼度の推定が可能になり，決定リストの性能が向上することを示す．本手法の有効性を確かめるために，語義曖昧性解消の問題に決定リストを適用して実験を行なった．英語に関してはSenseval-1 のデータを用い，日本語に関しては疑似単語を用いた．その結果，ベイズ学習による信頼度推定手法が，ルールの確率値の推定精度を高め，決定リストの分類性能を向上させることを確認した．","subitem_description_type":"Abstract"},{"subitem_description":"The decision list algorithm is one of the most successful algorithms for classification problems in natural language processing. The most important part of the decision list algorithm is the calculation of reliability for each rule, hence the estimation of probability for each contextual evidence. However, the majority of research efforts using decision lists do not think much of the estimation method. We propose an estimation method based on Bayesian learning which gives well-founded smoothing and better use of prior information on each type of contextual evidences. Experimental results obtained using Senseval-1 data set and Japanese pseudowords show that our method makes probability estimation more precise, leading to improvement of classification performance of the decision list algorithm.","subitem_description_type":"Abstract"}]},"item_2_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"106333","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Tsuruoka, Yoshimasa"}]},{"nameIdentifiers":[{"nameIdentifier":"106334","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Chikayama, Takashi"}]}]},"item_2_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"言語処理学会"}]},"item_2_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10472659","subitem_source_identifier_type":"NCID"}]},"item_2_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"13407619","subitem_source_identifier_type":"ISSN"}]},"item_2_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"007","subitem_subject_scheme":"NDC"}]},"item_2_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"The Association for Natural Language Processing"}]},"item_2_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"科学技術振興事業団"},{"subitem_text_value":"東京大学新領域創成科学研究科"},{"subitem_text_value":"Japan Science and Technology Corporation"},{"subitem_text_value":"School of Frontier Sciences, The University of Tokyo"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鶴岡, 慶雅"}],"nameIdentifiers":[{"nameIdentifier":"106331","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"近山, 隆"}],"nameIdentifiers":[{"nameIdentifier":"106332","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-06-26"}],"displaytype":"detail","filename":"v09n3_01.pdf","filesize":[{"value":"136.7 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"本文(fulltext)","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/411/files/v09n3_01.pdf"},"version_id":"21a1c061-8e5c-489b-bf00-20b41f831b3a"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"決定リスト","subitem_subject_scheme":"Other"},{"subitem_subject":"ベイズ学習","subitem_subject_scheme":"Other"},{"subitem_subject":"事前分布","subitem_subject_scheme":"Other"},{"subitem_subject":"語議曖昧性解消","subitem_subject_scheme":"Other"},{"subitem_subject":"Decision lists","subitem_subject_scheme":"Other"},{"subitem_subject":"Bayesian learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Prior distribution","subitem_subject_scheme":"Other"},{"subitem_subject":"Word sense disambiguation","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ベイズ統計の手法を利用した決定リストのルール信頼度推定法","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ベイズ統計の手法を利用した決定リストのルール信頼度推定法"}]},"item_type_id":"2","owner":"1","path":["15","112"],"pubdate":{"attribute_name":"公開日","attribute_value":"2009-12-14"},"publish_date":"2009-12-14","publish_status":"0","recid":"411","relation_version_is_last":true,"title":["ベイズ統計の手法を利用した決定リストのルール信頼度推定法"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T03:41:14.820550+00:00"}