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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,7,31]]},"abstract":"<jats:p>Question retrieval, which aims to find similar versions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging, mainly in regard to five aspects: synonymy, polysemy, word order, question length, and data sparsity. In this article, we propose a unified framework to simultaneously handle these five problems. We use the word combined with corresponding concept information to handle the synonymy problem and the polysemous problem. Concept embedding and word embedding are learned at the same time from both the context-dependent and context-independent views. To handle the word-order problem, we propose a high-level feature-embedded convolutional semantic model to learn question embedding by inputting concept embedding and word embedding. Due to the fact that the lengths of some questions are long, we propose a value-based convolutional attentional method to enhance the proposed high-level feature-embedded convolutional semantic model in learning the key parts of the question and the answer. The proposed high-level feature-embedded convolutional semantic model nicely represents the hierarchical structures of word information and concept information in sentences with their layer-by-layer convolution and pooling. Finally, to resolve data sparsity, we propose using the multi-view learning method to train the attention-based convolutional semantic model on question\u2013answer pairs. To the best of our knowledge, we are the first to propose simultaneously handling the above five problems in question retrieval using one framework. Experiments on three real question-answering datasets show that the proposed framework significantly outperforms the state-of-the-art solutions.<\/jats:p>","DOI":"10.1145\/3151957","type":"journal-article","created":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T13:10:52Z","timestamp":1517490652000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Concept and Attention-Based CNN for Question Retrieval in Multi-View Learning"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6412-5449","authenticated-orcid":false,"given":"Pengwei","family":"Wang","sequence":"first","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ji","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dejing","family":"Dou","sequence":"additional","affiliation":[{"name":"University of Oregon, Eugene, OR, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nisansa De","family":"Silva","sequence":"additional","affiliation":[{"name":"University of Oregon, Eugene, OR, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Weber State University, Ogden, UT, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,31]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944966"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_2_1_3_1","volume-title":"Mercer","author":"Brown Peter F.","year":"1993","unstructured":"Peter F. 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