{"links":{},"id":194257,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00194257","sets":["1164:3500:9687:9688"]},"path":["9688"],"owner":"44499","recid":"194257","title":["Twitterからの事象パターン知識の獲得"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-01-31"},"_buckets":{"deposit":"453d3943-317e-4d6c-beb8-be146851a5ec"},"_deposit":{"id":"194257","pid":{"type":"depid","value":"194257","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Twitterからの事象パターン知識の獲得","author_link":["459372","459371","459369","459370"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Twitterからの事象パターン知識の獲得"},{"subitem_title":"Knowledge Acquisition about Event Information from Twitter","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"対話応用","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-01-31","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"九州工業大学情報工学部"},{"subitem_text_value":"九州工業大学情報工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Artificial Intelligence, Kyushu Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Artificial Intelligence, Kyushu Institute of 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/194257/files/IPSJ-IFAT19133004.pdf","label":"IPSJ-IFAT19133004.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IFAT19133004.pdf","filesize":[{"value":"480.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"d2b5c342-27f7-424f-b6d2-2592c775ba3a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山元, 航平"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"嶋田, 和孝"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kouhei, Yamamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazutaka, Shimada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10114171","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":"2188-8884","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，雑談対話システムに有効な知識獲得の手法を提案する．雑談対話システムでは，適切な発話を行うために幅広い知識が必要となる．しかし，そのような知識の人手での作成は高コストである．また，どのような要素を知識化するかは自明でなく，網羅性の問題が生じる．この問題を解決するため，本論文では Twitter に着目し，Tweet 内容とその Tweet の投稿時間の関係を利用して，自動で時間情報を保持した知識の獲得を行う．具体的には，収集された Tweet から事象語 （動詞もしくは名詞と動詞のペア） を抽出し，5 つの時間区分における頻度分布 （事象パターン知識） を獲得する．さらに，この頻度分布から，どの時間にどのような事象が生じているのかという知識も自動的に獲得する．実験結果より，夜に “寝る” のような一般的な知識のみならず，4 月に “チャレンジする” などの興味深い事例も得られた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a knowledge acquisition method for non-task-oriented dialogue systems. Such dialogue systems need a wide variety of knowledge for generating appropriate and sophisticated responses. However, constructing such knowledge is costly. To solve this problem, we focus on a relation about each tweet and the posted time. First, we extract event words, such as verbs, from tweets. Then, we generate frequency distribution for five different time divisions, e.g., a monthly basis. We checked high ranked event words in each time division. As a result, we obtained not only common-sense things such as \"sleep\" in night but also interesting events such as \"challenge\" in April (April is the starting month in Japan.)","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告情報基礎とアクセス技術（IFAT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-01-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2019-IFAT-133"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T00:59:19.594742+00:00","updated":"2025-01-19T23:33:11.041903+00:00"}