Query Expansion for Microblog Retrieval Focusing on an Ensemble of Features

• Item type: Journal(1)
• 公開日: 2019-01-15
• タイトル: Query Expansion for Microblog Retrieval Focusing on an Ensemble of Features
• タイトル / 言語: en
• タイトル: Query Expansion for Microblog Retrieval Focusing on an Ensemble of Features
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] microblog search, query expansion, supervised learning, pseudo-relevance feedback, temporal information retrieval, convolutional long short-term memory, expansion term selection, word embedding
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Toyohashi University of Technology (TUT)
• 著者所属: Institut de Recherche en Informatique de Toulouse (IRIT), Université Toulouse III - Paul Sabatier
• 著者所属: Toyohashi University of Technology (TUT)
• 著者所属(英): en
• 著者所属(英): Toyohashi University of Technology (TUT)
• 著者所属(英): en
• 著者所属(英): Institut de Recherche en Informatique de Toulouse (IRIT), Université Toulouse III - Paul Sabatier
• 著者所属(英): en
• 著者所属(英): Toyohashi University of Technology (TUT)
• 著者名: Abu, Nowshed Chy; Md, Zia Ullah; Masaki, Aono
• 著者名(英): Abu, Nowshed Chy; Md, Zia Ullah; Masaki, Aono
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations. One way of alleviating this problem is to reformulate the query via query expansion. However, finding good expansion terms for a given query is a challenging task. In this paper, we present a query expansion framework, where supervised learning is adopted for selecting expansion terms. Upon retrieving tweets by our proposed topic modeling based query expansion, we utilize the pseudo-relevance feedback and a new temporal relatedness approach to select the candidate tweets. Next, we devise several new features to select the temporally and semantically relevant expansion terms by leveraging the temporal, word embedding, and sentiment association of candidate term and query. Moreover, we also utilize the lexical and twitter specific features to quantify the term relatedness. After supervised feature selection using regularized regression, we estimate the feature importance by applying random forest. Then, we make use of a learning-to-rank (L2R) framework to rank the candidate expansion terms. Results of extensive experiments on TREC Microblog 2011 and 2012 test collections over the Tweets2011 corpus show that our proposed method outperforms the baseline and competitive query expansion methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); DOI http://dx.doi.org/10.2197/ipsjjip.27.61; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations. One way of alleviating this problem is to reformulate the query via query expansion. However, finding good expansion terms for a given query is a challenging task. In this paper, we present a query expansion framework, where supervised learning is adopted for selecting expansion terms. Upon retrieving tweets by our proposed topic modeling based query expansion, we utilize the pseudo-relevance feedback and a new temporal relatedness approach to select the candidate tweets. Next, we devise several new features to select the temporally and semantically relevant expansion terms by leveraging the temporal, word embedding, and sentiment association of candidate term and query. Moreover, we also utilize the lexical and twitter specific features to quantify the term relatedness. After supervised feature selection using regularized regression, we estimate the feature importance by applying random forest. Then, we make use of a learning-to-rank (L2R) framework to rank the candidate expansion terms. Results of extensive experiments on TREC Microblog 2011 and 2012 test collections over the Tweets2011 corpus show that our proposed method outperforms the baseline and competitive query expansion methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); DOI http://dx.doi.org/10.2197/ipsjjip.27.61; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 60, 号 1, 発行日 2019-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764