{"created":"2021-03-01T06:16:56.121346+00:00","id":395,"links":{},"metadata":{"_buckets":{"deposit":"63f88f5a-def7-4030-9f4e-813b181caaa2"},"_deposit":{"id":"395","owners":[],"pid":{"revision_id":0,"type":"depid","value":"395"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00000395","sets":["34:95:96","9:10:15"]},"item_2_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"A Maximum Entropy Tagging Model with Unsupervised Hidden Markov Models"}]},"item_2_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2004-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"24","bibliographicPageStart":"3","bibliographicVolumeNumber":"11","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":"本論文では，教師なし学習によって推定された隠れマルコフモデル(HMM) の隠れ状態を最大エントロピー(ME) モデルの素性として利用するタグ付けモデルを提案する．教師なし学習された確率モデルを本手法に従って利用することにより，タグ付きコーパスが少ない状況でのタグ付け器作成コストを削減することが可能となる．実験では，英語品詞タグ付けと日本語の単語分割を対象として，少量のタグ付きコーパスで学習する場合の精度が本手法により改善されることを示し，提案手法がタグ付け器作成のコスト削減に寄与することを実証する．さらに，英語品詞タグ付けでタグ付きコーパスを最大限利用できる場合には，最高水準の精度(96.84 %) を達成し，品詞タグ付けモデルとしても優れていることを示す．","subitem_description_type":"Abstract"},{"subitem_description":"We describe a new tagging model where the states of a hidden Markov model (HMM) estimated by unsupervised learning are incorporated as the features in a maximum entropy model. Our method for exploiting unsupervised learning of a probabilistic model can reduce the cost of building taggers with a small annotated corpus. Experimental results on English POS tagging and Japanese word segmentation show that our method greatly improves the tagging accuracy when the model is trained with a small annotated corpus. Furthermore, our English POS tagger achieved a state-of-the-art POS tagging accuracy (96.84 %) when a large annotated corpus is available.","subitem_description_type":"Abstract"}]},"item_2_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"106261","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Kazama, Jun’ichi"}]},{"nameIdentifiers":[{"nameIdentifier":"106262","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Miyao, Yusuke"}]},{"nameIdentifiers":[{"nameIdentifier":"106263","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Tsujii, Jun’ichi"}]}]},"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":"北陸先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報学環"},{"subitem_text_value":"科学技術振興機構CREST, CREST"},{"subitem_text_value":"School of Information Science, Japan Advanced Institute of Science and Technology"},{"subitem_text_value":"Graduate School of Information Science and Technology, University of Tokyo"},{"subitem_text_value":"Graduate School of Interdisciplinary Information Studies, University of Tokyo"},{"subitem_text_value":"Japan Science and Technology Agency"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"風間, 淳一"}],"nameIdentifiers":[{"nameIdentifier":"106258","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"宮尾, 祐介"}],"nameIdentifiers":[{"nameIdentifier":"106259","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"辻井, 潤一"}],"nameIdentifiers":[{"nameIdentifier":"106260","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":"v11n4_01.pdf","filesize":[{"value":"252.2 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"v11n4_01.pdf","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/395/files/v11n4_01.pdf"},"version_id":"10afcf4a-e9ec-4319-b952-0270a36403e7"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"タグ付け","subitem_subject_scheme":"Other"},{"subitem_subject":"最大エントロピー法","subitem_subject_scheme":"Other"},{"subitem_subject":"教師なし学習","subitem_subject_scheme":"Other"},{"subitem_subject":"隠れマルコフモデル","subitem_subject_scheme":"Other"},{"subitem_subject":"Tagging","subitem_subject_scheme":"Other"},{"subitem_subject":"Maximum Entorpy Method","subitem_subject_scheme":"Other"},{"subitem_subject":"Unsupervised Learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Hidden Markov Model","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":"教師なし隠れマルコフモデルを利用した最大エントロピータグ付けモデル","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"教師なし隠れマルコフモデルを利用した最大エントロピータグ付けモデル"}]},"item_type_id":"2","owner":"1","path":["15","96"],"pubdate":{"attribute_name":"公開日","attribute_value":"2009-09-08"},"publish_date":"2009-09-08","publish_status":"0","recid":"395","relation_version_is_last":true,"title":["教師なし隠れマルコフモデルを利用した最大エントロピータグ付けモデル"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T03:41:17.554711+00:00"}