{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:02:39Z","timestamp":1770271359741,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"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":"publisher","award":["71771113"],"award-info":[{"award-number":["71771113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2019YFC0810705,2018YFC0807000"],"award-info":[{"award-number":["2019YFC0810705,2018YFC0807000"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,23]]},"DOI":"10.1145\/3478905.3478981","type":"proceedings-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T15:58:28Z","timestamp":1632844708000},"page":"389-395","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Comparison between Calculation Methods for Semantic Text Similarity based on Siamese Networks"],"prefix":"10.1145","author":[{"given":"Keyang","family":"Wang","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Zeng","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Feiyu","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Yang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"4","author":"Song Y.Y.","year":"2020","unstructured":"Song , Y.Y. , , An unsupervised method on question similarity matching. Application Research of Computers , 2020 (S02): p. 4 . Song, Y.Y., , An unsupervised method on question similarity matching. Application Research of Computers, 2020(S02): p. 4.","journal-title":"Application Research of Computers"},{"key":"e_1_3_2_2_2_1","volume-title":"Computer Speech & Language","author":"Chen Q.","year":"2019","unstructured":"Chen , Q. and W. Wa Ng , Sequential Attention-based Network for Noetic End-to-End Response Selection . Computer Speech & Language , 2019 . Chen, Q. and W. Wa Ng, Sequential Attention-based Network for Noetic End-to-End Response Selection. Computer Speech & Language, 2019."},{"key":"e_1_3_2_2_3_1","volume-title":"A. Siamese Recurrent Architectures for Learning Sentence Similarity. in Thirtieth Aaai Conference on Artificial Intelligence.","author":"Thyagarajan","year":"2016","unstructured":"Thyagarajan , A. Siamese Recurrent Architectures for Learning Sentence Similarity. in Thirtieth Aaai Conference on Artificial Intelligence. 2016 . Thyagarajan, A. Siamese Recurrent Architectures for Learning Sentence Similarity. in Thirtieth Aaai Conference on Artificial Intelligence. 2016."},{"key":"e_1_3_2_2_4_1","first-page":"13","volume-title":"IJCA","author":"Gomaa W.","year":"2014","unstructured":"H. Gomaa , W. and A.A. Fahmy , A Survey of Text Similarity Approaches . IJCA , 2014 . 68(13): p. 13 - 18 . H. Gomaa, W. and A.A. Fahmy, A Survey of Text Similarity Approaches. IJCA, 2014. 68(13): p. 13-18."},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the","author":"Winkler W.E.","unstructured":"Winkler , W.E. , String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage . Proceedings of the , 1990: p. 8. Winkler, W.E., String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage. Proceedings of the, 1990: p. 8."},{"key":"e_1_3_2_2_6_1","volume-title":"Distant Language Pairs. in COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference","author":"Barr\u00f3n-Cedeo A.b.r.","year":"2010","unstructured":"Barr\u00f3n-Cedeo , A.b.r. , Plagiarism Detection across Distant Language Pairs. in COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference , 23-27 August 2010 , Beijing, China. 2010. Barr\u00f3n-Cedeo, A.b.r., Plagiarism Detection across Distant Language Pairs. in COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23-27 August 2010, Beijing, China. 2010."},{"key":"e_1_3_2_2_7_1","volume-title":"Taxicab Geometry: an adventure in non-Euclidean geometry","author":"Krause E.F.","year":"1987","unstructured":"Krause , E.F. , Taxicab Geometry: an adventure in non-Euclidean geometry . 1987 , New York : Dover Publications . viii, 88 p. Krause, E.F., Taxicab Geometry: an adventure in non-Euclidean geometry. 1987, New York: Dover Publications. viii, 88 p."},{"key":"e_1_3_2_2_8_1","volume-title":"Etude comparative de la distribution florale dans une portion des Alpes et des Jura. bulletin del la societe vaudoise des sciences naturelles","author":"Jaccard P.","year":"1901","unstructured":"Jaccard , P. , Etude comparative de la distribution florale dans une portion des Alpes et des Jura. bulletin del la societe vaudoise des sciences naturelles , 1901 . 37(142): p. 547-579. Jaccard, P., Etude comparative de la distribution florale dans une portion des Alpes et des Jura. bulletin del la societe vaudoise des sciences naturelles, 1901. 37(142): p. 547-579."},{"key":"e_1_3_2_2_9_1","volume-title":"Ecology","author":"Dice L.R.","year":"1945","unstructured":"Dice , L.R. , Measures of the Amount of Ecologic Association Between Species . Ecology , 1945 . 26(3). Dice, L.R., Measures of the Amount of Ecologic Association Between Species. Ecology, 1945. 26(3)."},{"key":"e_1_3_2_2_10_1","author":"Landauer T.K.","year":"1997","unstructured":"Landauer , T.K. Dumais, and T. Susan , A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review , 1997 . Landauer, T.K. Dumais, and T. Susan, A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 1997.","journal-title":"Psychological Review"},{"key":"e_1_3_2_2_11_1","first-page":"2016","author":"Neculoiu P.","year":"2016","unstructured":"Neculoiu , P. , M. Versteegh , and M. Rotaru . Learning Text Similarity with Siamese Recurrent Networks. in Repl4NLP workshop at ACL 2016 . 2016 . Neculoiu, P., M. Versteegh, and M. Rotaru. Learning Text Similarity with Siamese Recurrent Networks. in Repl4NLP workshop at ACL2016. 2016.","journal-title":"Learning Text Similarity with Siamese Recurrent Networks. in Repl4NLP workshop at ACL"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_2_13_1","volume-title":"A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations","author":"Wan S.","year":"2015","unstructured":"Wan , S. , , A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations . AAAI Press , 2015 . Wan, S., , A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations. AAAI Press, 2015."},{"key":"e_1_3_2_2_14_1","volume-title":"Convolutional Neural Network Architectures for Matching Natural Language Sentences. Advances in neural information processing systems","author":"Hu B.","year":"2015","unstructured":"Hu , B. , , Convolutional Neural Network Architectures for Matching Natural Language Sentences. Advances in neural information processing systems , 2015 . 3. Hu, B., , Convolutional Neural Network Architectures for Matching Natural Language Sentences. Advances in neural information processing systems, 2015. 3."},{"key":"e_1_3_2_2_15_1","first-page":"731","volume-title":"Computers & Graphics","author":"Wan S.","year":"2016","unstructured":"Wan , S. , , Match- SRNN : Modeling the Recursive Matching Structure with Spatial RNN . Computers & Graphics , 2016 . 28(5): p. 731 - 745 . Wan, S., , Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN. Computers & Graphics, 2016. 28(5): p. 731-745."},{"key":"e_1_3_2_2_16_1","volume-title":"A Deep Relevance Matching Model for Ad-hoc Retrieval","author":"Guo J.","year":"2017","unstructured":"Guo , J. , , A Deep Relevance Matching Model for Ad-hoc Retrieval . 2017 . Guo, J., , A Deep Relevance Matching Model for Ad-hoc Retrieval. 2017."},{"key":"e_1_3_2_2_17_1","volume-title":"Enhanced LSTM for Natural Language Inference. in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).","author":"Chen Q.","year":"2016","unstructured":"Chen , Q. , Enhanced LSTM for Natural Language Inference. in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016 . Chen, Q., Enhanced LSTM for Natural Language Inference. in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016."},{"key":"e_1_3_2_2_18_1","first-page":"775","author":"Mihalcea R.","year":"2006","unstructured":"Mihalcea , R. , C. Corley , and C. Strapparava , Corpus-based and Knowledge-based Measures of Text Semantic Similarity. Unt Scholarly Works , 2006 . 1: p. 775 \u2013 780 . Mihalcea, R., C. Corley, and C. Strapparava, Corpus-based and Knowledge-based Measures of Text Semantic Similarity. Unt Scholarly Works, 2006. 1: p. 775\u2013780.","journal-title":"Corpus-based and Knowledge-based Measures of Text Semantic Similarity. Unt Scholarly Works"},{"key":"e_1_3_2_2_19_1","volume-title":"Glove: Global Vectors for Word Representation. in Conference on Empirical Methods in Natural Language Processing.","author":"Pennington J.","year":"2014","unstructured":"Pennington , J. , R. Socher , and C. Manning . Glove: Global Vectors for Word Representation. in Conference on Empirical Methods in Natural Language Processing. 2014 . Pennington, J., R. Socher, and C. Manning. Glove: Global Vectors for Word Representation. in Conference on Empirical Methods in Natural Language Processing. 2014."},{"key":"e_1_3_2_2_20_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin J.","year":"2018","unstructured":"Devlin , J. , , BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . 2018 . Devlin, J., , BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2018."},{"key":"e_1_3_2_2_21_1","volume-title":"a distilled version of BERT: smaller, faster, cheaper and lighter","author":"Sanh V.","year":"2019","unstructured":"Sanh , V. , , Distil BERT , a distilled version of BERT: smaller, faster, cheaper and lighter . 2019 . Sanh, V., , DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 2019."},{"key":"e_1_3_2_2_22_1","volume-title":"Convolutional Neural Networks for Sentence Classification. Eprint Arxiv","author":"Kim Y.","year":"2014","unstructured":"Kim , Y. , Convolutional Neural Networks for Sentence Classification. Eprint Arxiv , 2014 . Kim, Y., Convolutional Neural Networks for Sentence Classification. Eprint Arxiv, 2014."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1162"},{"key":"e_1_3_2_2_24_1","volume-title":"SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation","author":"Cer D.","year":"2017","unstructured":"Cer , D. , , SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation . 2017 . Cer, D., , SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation. 2017."},{"key":"e_1_3_2_2_25_1","volume-title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","author":"Reimers N.","year":"2019","unstructured":"Reimers , N. and I. Gurevych , Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks . 2019 . Reimers, N. and I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. 2019."}],"event":{"name":"DSIT 2021: 2021 4th International Conference on Data Science and Information Technology","location":"Shanghai China","acronym":"DSIT 2021"},"container-title":["2021 4th International Conference on Data Science and Information Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478905.3478981","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3478905.3478981","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:38Z","timestamp":1750191518000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478905.3478981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":25,"alternative-id":["10.1145\/3478905.3478981","10.1145\/3478905"],"URL":"https:\/\/doi.org\/10.1145\/3478905.3478981","relation":{},"subject":[],"published":{"date-parts":[[2021,7,23]]},"assertion":[{"value":"2021-09-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}