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Process."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>The methods based on the combination of word-level and character-level features can effectively boost performance on Chinese short text classification. A lot of works concatenate two-level features with little processing, which leads to losing feature information. In this work, we propose a novel framework called Mutual-Attention Convolutional Neural Networks, which integrates word and character-level features without losing too much feature information. We first generate two matrices with aligned information of two-level features by multiplying word and character features with a trainable matrix. Then, we stack them as a three-dimensional tensor. Finally, we generate the integrated features using a convolutional neural network. Extensive experiments on six public datasets demonstrate improved performance of our new framework over current methods.<\/jats:p>","DOI":"10.1145\/3388970","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:07Z","timestamp":1594125547000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Chinese Short Text Classification with Mutual-Attention Convolutional Neural Networks"],"prefix":"10.1145","volume":"19","author":[{"given":"Ming","family":"Hao","sequence":"first","affiliation":[{"name":"University of Science and Technology Beijing, Beijing Shi, China"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jing-Yi","family":"Liang","sequence":"additional","affiliation":[{"name":"China University of Geosciences, Wuhan, Wuhan Shi, Hubei, China"}]},{"given":"Bo-Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou Shi, Zhejiang, China"}]},{"given":"Xu-Cheng","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Science and Technology Beijing, Haidian Qu, Beijing Shi, China"}]}],"member":"320","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)","author":"Abadi Mart\u00edn","year":"2016"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Bahdanau Dzmitry","year":"2014"},{"key":"e_1_2_1_4_1","volume-title":"ClassiNet -- Predicting missing features for short-text classification. 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