{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:31:14Z","timestamp":1776177074665,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T00:00:00Z","timestamp":1522627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Nos. 61672127, 61173100"],"award-info":[{"award-number":["Nos. 61672127, 61173100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"crossref","award":["No.15BYY175"],"award-info":[{"award-number":["No.15BYY175"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"crossref"}]}],"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>Word embedding-based methods have received increasing attention for their flexibility and effectiveness in many natural language-processing (NLP) tasks, including Word Similarity (WS). However, these approaches rely on high-quality corpus and neglect prior knowledge. Lexicon-based methods concentrate on human\u2019s intelligence contained in semantic resources, e.g., Tongyici Cilin, HowNet, and Chinese WordNet, but they have the drawback of being unable to deal with unknown words. This article proposes a three-stage framework for measuring the Chinese word similarity by incorporating prior knowledge obtained from lexicons and statistics into word embedding: in the first stage, we utilize retrieval techniques to crawl the contexts of word pairs from web resources to extend context corpus. In the next stage, we investigate three types of single similarity measurements, including lexicon similarities, statistical similarities, and embedding-based similarities. Finally, we exploit simple combination strategies with math operations and the counter-fitting combination strategy using optimization method. To demonstrate our system\u2019s efficiency, comparable experiments are conducted on the PKU-500 dataset. Our final results are 0.561\/0.516 of Spearman\/Pearson rank correlation coefficient, which outperform the state-of-the-art performance to the best of our knowledge. Experiment results on Chinese MC-30 and SemEval-2012 datasets show that our system also performs well on other Chinese datasets, which proves its transferability. Besides, our system is not language-specific and can be applied to other languages, e.g., English.<\/jats:p>","DOI":"10.1145\/3182622","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T12:12:24Z","timestamp":1522671144000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Incorporating Prior Knowledge into Word Embedding for Chinese Word Similarity Measurement"],"prefix":"10.1145","volume":"17","author":[{"given":"Degen","family":"Huang","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Liaoning, China"}]},{"given":"Jiahuan","family":"Pei","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Liaoning, China"}]},{"given":"Cong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Liaoning, China"}]},{"given":"Kaiyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Liaoning, China"}]},{"given":"Jianjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Liaoning, China"}]}],"member":"320","published-online":{"date-parts":[[2018,4,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-012-0135-3"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242675"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1011"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Zhendong Dong and Qiang Dong. 2006. HowNet and the Computation of Meaning. World Scientific Singapore.   Zhendong Dong and Qiang Dong. 2006. HowNet and the Computation of Meaning. World Scientific Singapore.","DOI":"10.1142\/5935"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of Fuzzy Systems and Knowledge Discovery. 1487--1492","author":"Fan Mengjia","year":"2015"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of North American Chapter of the. Association for Computational Linguistics: Human Language Technologies (HLT-NAACL\u201915)","author":"Faruqui Manaal"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50496-4_67"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1515\/9783110197549.265"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00237"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1010"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/WI-IAT.2013.184"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1145"},{"key":"e_1_2_1_13_1","unstructured":"Qun Liu and Sujian Li. 2002. Word similarity computing based on how-net. Comput. Linguist. Chinese Lang. Process. 7 2 (2002).  Qun Liu and Sujian Li. 2002. Word similarity computing based on how-net. Comput. Linguist. Chinese Lang. Process. 7 2 (2002)."},{"key":"e_1_2_1_14_1","unstructured":"Jiaju Mei Yiming Zhu Yunqi Gao and Hongxiang Yin. 1983. Tongyici Cilin. Shanghai Lexicon Publishing Company Shanghai.  Jiaju Mei Yiming Zhu Yunqi Gao and Hongxiang Yin. 1983. Tongyici Cilin. Shanghai Lexicon Publishing Company Shanghai."},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of Workshop at the International Conference on Learning Representations (ICLR\u201913)","author":"Mikolov Tomas","year":"2013"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS\u201913)","author":"Mikolov Tomas","year":"2013"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the North American Chapter of the. Association for Computational Linguistics: Human Language Technologies (HLT-NAACL\u201916)","author":"Mrk\u0161i\u0107 Nikola"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2074"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1100"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50496-4_69"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the Conference on Empirical Methods for Natural Language Processing (EMNLP\u201914)","author":"Pennington Jeffrey"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1173"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.48"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the 6th International Joint Conference on Natural Language Processing. 10--18","author":"Shan Wang","year":"2013"},{"key":"e_1_2_1_25_1","first-page":"602","article-title":"Words similarity algorithm based on tongyici cilin in semantic web adaptive learning system","volume":"28","author":"Tian Jiule","year":"2010","journal-title":"J. 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