{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:11:14Z","timestamp":1750306274029,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T00:00:00Z","timestamp":1517356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>This article addresses the problem of learning compositional Chinese sentence representations, which represent the meaning of a sentence by composing the meanings of its constituent words. In contrast to English, a Chinese word is composed of characters, which contain rich semantic information. However, this information has not been fully exploited by existing methods. In this work, we introduce a novel, mixed character-word architecture to improve the Chinese sentence representations by utilizing rich semantic information of inner-word characters. We propose two novel strategies to reach this purpose. The first one is to use a mask gate on characters, learning the relation among characters in a word. The second one is to use a max-pooling operation on words to adaptively find the optimal mixture of the atomic and compositional word representations. Finally, the proposed architecture is applied to various sentence composition models, which achieves substantial performance gains over baseline models on sentence similarity task. To further verify the generalization ability of our model, we employ the learned sentence representations as features in sentence classification task, question classification task, and sentence entailment task. Results have shown that the proposed mixed character-word sentence representation models outperform both the character-based and word-based models.<\/jats:p>","DOI":"10.1145\/3156778","type":"journal-article","created":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T13:10:52Z","timestamp":1517490652000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Empirical Exploring Word-Character Relationship for Chinese Sentence Representation"],"prefix":"10.1145","volume":"17","author":[{"given":"Shaonan","family":"Wang","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 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":"10.25080\/Majora-92bf1922-003"},{"volume-title":"Proceedings of the 54th Annual Meetings of the Association for Computational Linguistics. 1466--1477","author":"Bowman Samuel R.","key":"e_1_2_1_2_1"},{"volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence. 1236--1242","year":"2015","author":"Chen Xinxiong","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1177"},{"key":"e_1_2_1_5_1","unstructured":"Sander Dieleman Jan Schl\u00fcter Colin Raffel Eben Olson S\u00f8ren Kaae S\u00f8nderby Daniel Nouri Daniel Maturana Martin Thoma Eric Battenberg and Jack Kelly et al. 2015. Lasagne: First release. Zenodo: Geneva Switzerland.  Sander Dieleman Jan Schl\u00fcter Colin Raffel Eben Olson S\u00f8ren Kaae S\u00f8nderby Daniel Nouri Daniel Maturana Martin Thoma Eric Battenberg and Jack Kelly et al. 2015. Lasagne: First release. Zenodo: Geneva Switzerland."},{"volume-title":"Proceedings of the 12th Annual Conference of the North American Chapter of the Association for Computational Linguistics. 758--764","year":"2013","author":"Ganitkevitch Juri","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1020"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1162"},{"volume-title":"Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. 873--882","author":"Huang Eric H.","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1162"},{"volume-title":"Proceedings of the 2014 Conference On Empirical Methods in Natural Language Processing. 2039--2048","author":"Jiwei Li.","key":"e_1_2_1_11_1"},{"key":"e_1_2_1_12_1","unstructured":"Dimitri Kartsaklis. 2015. Compositional distributional semantics with compact closed categories and Frobenius algebras. arXiv preprint arXiv:1505.00138.  Dimitri Kartsaklis. 2015. Compositional distributional semantics with compact closed categories and Frobenius algebras. arXiv preprint arXiv:1505.00138."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1089"},{"volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2741--2749","author":"Kim Yoon","key":"e_1_2_1_14_1"},{"volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.","year":"2014","author":"Kingma Diederik","key":"e_1_2_1_15_1"},{"key":"e_1_2_1_16_1","unstructured":"Ryan Kiros Yukun Zhu Ruslan R. Salakhutdinov Richard Zemel Raquel Urtasun Antonio Torralba and Sanja Fidler. 2015. Skip-thought vectors. Advances in Neural Information Processing Systems. 3294--3302.   Ryan Kiros Yukun Zhu Ruslan R. Salakhutdinov Richard Zemel Raquel Urtasun Antonio Torralba and Sanja Fidler. 2015. Skip-thought vectors. Advances in Neural Information Processing Systems. 3294--3302."},{"volume-title":"Proceedings of the 31st International Conference on Machine Learning. 1188--1196","author":"Quoc","key":"e_1_2_1_17_1"},{"key":"e_1_2_1_18_1","first-page":"54","article-title":"A quantitative analysis of the transparency of lexical meaning in modern Chinese dictionary","volume":"3","author":"Li Jinxia","year":"2011","journal-title":"Chinese Linguistics"},{"key":"e_1_2_1_19_1","unstructured":"Zhouhan Lin Minwei Feng Cicero Nogueira dos Santos Mo Yu Bing Xiang Bowen Zhou and Yoshua Bengio. 2017. A self-attentive sentence embedding. arXiv preprint arXiv:1703.03130.  Zhouhan Lin Minwei Feng Cicero Nogueira dos Santos Mo Yu Bing Xiang Bowen Zhou and Yoshua Bengio. 2017. A self-attentive sentence embedding. arXiv preprint arXiv:1703.03130."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bandl.2013.04.002"},{"key":"e_1_2_1_21_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv: 1301.3781.  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv: 1301.3781."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1551-6709.2010.01106.x"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1209"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2016.2520371"},{"volume-title":"Proceedings of the 26th International Conference on Computational Linguistics. 309--318","year":"2016","author":"Rei Marek","key":"e_1_2_1_25_1"},{"volume-title":"Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 109--117","author":"Reisinger Joseph","key":"e_1_2_1_26_1"},{"volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. 151--161","author":"Socher Richar","key":"e_1_2_1_27_1"},{"volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 1556--1566","author":"Tai Kai Sheng","key":"e_1_2_1_28_1"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3171837.3171864"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1029"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3010088"},{"volume-title":"Proceedings of the 4th International Conference on Learning Representations.","year":"2016","author":"Wieting John","key":"e_1_2_1_32_1"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1157"},{"volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.","year":"2017","author":"Wieting John","key":"e_1_2_1_34_1"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/1855053"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1119"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00097"},{"volume-title":"Proceedings of the 23rd International Conference on Computational Linguistics. 1263--1271","year":"2010","author":"Zanzotto Fabio Massimo","key":"e_1_2_1_38_1"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3156778","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3156778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:38:44Z","timestamp":1750221524000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3156778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,31]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,9,30]]}},"alternative-id":["10.1145\/3156778"],"URL":"https:\/\/doi.org\/10.1145\/3156778","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2018,1,31]]},"assertion":[{"value":"2017-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-01-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}