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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,9,30]]},"abstract":"<jats:p>The intelligent question answering system aims to provide quick and concise feedback on the questions of users. Although the performance of phrase-level and numerous attention models have been improved, the sentence components and position information are not emphasized enough. This article combines Ci-Lin and word2vec to divide all of the words in the question-answer pairs into groups according to the semantics and select one kernel word in each group. The remaining words are common words and realize the semantic mapping mechanism between kernel words and common words. With this Chinese semantic mapping mechanism, the common words in all questions and answers are replaced by the semantic kernel words to realize the normalization of the semantic representation. Meanwhile, based on the bi-directional LSTM model, this article introduces a method of the combination of semantic role labeling and positional context, dividing the sentence into multiple semantic segments according to semantic logic. The weight is given to the neighboring words in the same semantic segment and propose semantic role labeling position attention based on the bi-directional LSTM model (BLSTM-SRLP). The good performance of the BLSTM-SRLP model has been demonstrated in comparative experiments on the food safety field dataset (FS-QA).<\/jats:p>","DOI":"10.1145\/3439800","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T20:06:29Z","timestamp":1625083589000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System"],"prefix":"10.1145","volume":"20","author":[{"given":"Mingwen","family":"Bi","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingchuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zuo","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zelong","family":"Xu","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyu","family":"Jin","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2328-2"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.01.008"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.11.002"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3137853.3138120"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1386-6"},{"key":"e_1_2_1_6_1","unstructured":"D. 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Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969173"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009941"},{"volume-title":"Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL\u201907)","author":"Wang M.","key":"e_1_2_1_10_1","unstructured":"M. Wang , N. A. Smith , and T. Mitamura . 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA . In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL\u201907) . 1\u201311. M. Wang, N. A. Smith, and T. Mitamura. 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA. 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Wang and E. Nyberg. 2015. A long short-term memory model for answer sentence selection in question answering. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 707\u2013712."},{"key":"e_1_2_1_22_1","volume-title":"ADADELTA: An adaptive learning rate method. arXiv:1212.5701.","author":"Zeiler M. D.","year":"2012","unstructured":"M. D. Zeiler . 2012 . ADADELTA: An adaptive learning rate method. arXiv:1212.5701. M. D. Zeiler. 2012. 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