{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212209","sets":["1164:4179:10535:10649"]},"path":["10649"],"owner":"44499","recid":"212209","title":["確率的潜在意味スケーリング"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-20"},"_buckets":{"deposit":"3972561e-f983-4b72-a6fb-66a17b074706"},"_deposit":{"id":"212209","pid":{"type":"depid","value":"212209","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"確率的潜在意味スケーリング","author_link":["540907","540908"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"確率的潜在意味スケーリング"},{"subitem_title":"Probabilistic Latent Semantic Scaling","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-07-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"統計数理研究所数理・推論研究系"}]},"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"}]},"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/212209/files/IPSJ-NL21249009.pdf","label":"IPSJ-NL21249009.pdf"},"date":[{"dateType":"Available","dateValue":"2023-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL21249009.pdf","filesize":[{"value":"15.4 MB"}],"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":"124e3f6e-e372-4eec-9993-6675252df4cb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 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"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daichi, Mochihashi","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8779","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"テキストをある意味的な軸に沿って連続的に測ることは，社会科学・人文科学などにも様々な応用を持つ重要な問題である．本研究ではこのために，項目反応理論とニューラル単語ベクトルに基づいて，政治学方法論の分野で提案された潜在意味スケーリング (LSS) の考え方を統計モデルとして実現する，確率的潜在意味スケーリング (PLSS) を提案する．また，分析対象となるテキストをキーワードに基づいて選択するための枠組として，潜在トピックモデルおよびニューラル文書ベクトルに基づく二種類の統計的な方法を提案する．政治学分野での公開データを用いた実験により，PLSS は LSS より人間の評価と高い相関を見せることを確認し，また LSS を確率モデルとして捉えることで，様々な統計的拡張を可能にする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Measuring text in a continuous scale is a fundamental problem that has many applications including social sciences and humanities. This paper proposes a probabilistic extension to Latent Semantic Scaling (LSS) in political methodology, called Probabilistic Latent Semantic Scaling (PLSS). Leveraging Item Response Theory and neural word vectors, we showed that PLSS consistently outperforms LSS in terms of relevance to human evaluation. We also proposed two statistical method to select texts to be analyzed that are associated with the given keywords. Probabilistic treatment of LSS enables many future extensions, including handling missing data and time series analysis.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"16","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2021-NL-249"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":212209,"updated":"2025-01-19T17:34:28.589727+00:00","links":{},"created":"2025-01-19T01:13:12.221521+00:00"}