{"updated":"2025-01-21T14:04:59.652618+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00095175","sets":["1164:4179:6969:7262"]},"path":["7262"],"owner":"11","recid":"95175","title":["ガウス過程に基づく連続空間トピックモデル"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-09-05"},"_buckets":{"deposit":"ac568741-1451-47f0-8530-6e8c75def0d4"},"_deposit":{"id":"95175","pid":{"type":"depid","value":"95175","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"ガウス過程に基づく連続空間トピックモデル","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ガウス過程に基づく連続空間トピックモデル"},{"subitem_title":"Modeling Text through Gaussian Processes","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"形態素・学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2013-09-05","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"統計数理研究所"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"産業技術総合研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The Institute of Statistical Mathematics","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/95175/files/IPSJ-NL13213011.pdf"},"date":[{"dateType":"Available","dateValue":"2015-09-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL13213011.pdf","filesize":[{"value":"903.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c77e91ce-1097-4f07-9407-c63156c37a0a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2013 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"持橋, 大地"},{"creatorName":"吉井, 和佳"},{"creatorName":"後藤, 真孝"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daichi, Mochihashi","creatorNameLang":"en"},{"creatorName":"Kazuyoshi, Yoshii","creatorNameLang":"en"},{"creatorName":"Masataka, Goto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では，単語に潜在空間における座標を明示的に与え，その上でのガウス過程を考えることで，通常の混合モデルに基づくトピックモデルより高精度なテキストモデルが得られることを示す．提案法は潜在層が二値ではなく，ガウス分布に従う RBM の生成モデルともみることができ，MCMC により単語の潜在座標を学習することは他の多くの応用や，可視化にも自然に繋がることができる．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper proposes a continous space text model based on Gaussian processes. Introducing latent coordinates of words over which the Gaussian process is defined, we can encode word correlations directly and lead to a model that performs better than mixture models. Our model would serve as a foundation of more complex text models and also as a statistical visualization of texts.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2013-09-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2013-NL-213"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:42:19.951774+00:00","id":95175,"links":{}}