{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:59:42Z","timestamp":1765486782635,"version":"3.40.5"},"reference-count":71,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Metonymy resolution (MR) is a challenging task in the field of natural language processing. The task of MR aims to identify the metonymic usage of a word that employs an entity name to refer to another target entity. Recent BERT-based methods yield state-of-the-art performances. However, they neither make full use of the entity information nor explicitly consider syntactic structure. In contrast, in this paper, we argue that the metonymic process should be completed in a collaborative manner, relying on both lexical semantics and syntactic structure (syntax). This paper proposes a novel approach to enhancing BERT-based MR models with hard and soft syntactic constraints by using different types of convolutional neural networks to model dependency parse trees. Experimental results on benchmark datasets (e.g., <jats:sc>ReLocaR<\/jats:sc>, <jats:sc>SemEval<\/jats:sc> 2007 and <jats:sc>WiMCor<\/jats:sc>) confirm that leveraging syntactic information into fine pre-trained language models benefits MR tasks.<\/jats:p>","DOI":"10.1017\/s135132492200033x","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T13:41:35Z","timestamp":1659361295000},"page":"669-692","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":3,"title":["An empirical study of incorporating syntactic constraints into BERT-based location metonymy resolution"],"prefix":"10.1017","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1089-9828","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Siyuan","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Lingyi","family":"Meng","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"S135132492200033X_ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1024"},{"key":"S135132492200033X_ref66","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1206"},{"key":"S135132492200033X_ref70","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1244"},{"key":"S135132492200033X_ref33","unstructured":"Lin, C. , Miller, T. , Dligach, D. , Bethard, S. and Savova, G. (2019b). A BERT-based universal model for both within-and cross-sentence clinical temporal relation extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 65\u201371."},{"key":"S135132492200033X_ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"S135132492200033X_ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1105"},{"key":"S135132492200033X_ref50","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"S135132492200033X_ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00132"},{"key":"S135132492200033X_ref3","doi-asserted-by":"publisher","DOI":"10.3115\/1621474.1621583"},{"key":"S135132492200033X_ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1060"},{"key":"S135132492200033X_ref26","doi-asserted-by":"publisher","DOI":"10.1207\/s15327868ms0203_4"},{"key":"S135132492200033X_ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.330"},{"key":"S135132492200033X_ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-009-9087-y"},{"key":"S135132492200033X_ref25","doi-asserted-by":"publisher","DOI":"10.1515\/cogl.1998.9.1.37"},{"key":"S135132492200033X_ref68","unstructured":"Zarcone, A. , Utt, J. and Pad\u00f3, S. (2012). Modeling covert event retrieval in logical metonymy: Probabilistic and distributional accounts. In Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012), Montr\u00e9al, Canada. Association for Computational Linguistics, pp. 70\u201379."},{"key":"S135132492200033X_ref16","unstructured":"Hobbs, J.R. and Martin, P. 1987. Local pragmatics. Technical report. SRI International Menlo Park CA Artificial Intelligence Center, pp. 520\u2013523."},{"key":"S135132492200033X_ref22","doi-asserted-by":"publisher","DOI":"10.3115\/1219044.1219066"},{"key":"S135132492200033X_ref27","first-page":"25","article-title":"Metap","volume":"23","author":"Lakoff","year":"1991","journal-title":"Peace Research"},{"key":"S135132492200033X_ref54","first-page":"409","article-title":"The generative lexicon","volume":"17","author":"Pustejovsky","year":"1991","journal-title":"Computational Linguistics"},{"key":"S135132492200033X_ref64","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"S135132492200033X_ref40","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2013-596"},{"key":"S135132492200033X_ref57","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1117"},{"key":"S135132492200033X_ref60","unstructured":"Sun, C. , Huang, L. and Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp. 380\u2013385."},{"key":"S135132492200033X_ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1356"},{"key":"S135132492200033X_ref63","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.344"},{"key":"S135132492200033X_ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1264"},{"key":"S135132492200033X_ref8","doi-asserted-by":"crossref","unstructured":"Fass, D. (1988). Metonymy and metaphor: What\u2019s the difference? In Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics. International Committee on Computational Linguistics, pp. 177\u2013181.","DOI":"10.3115\/991635.991671"},{"key":"S135132492200033X_ref5","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"S135132492200033X_ref48","doi-asserted-by":"publisher","DOI":"10.3115\/1075096.1075104"},{"key":"S135132492200033X_ref29","first-page":"453","volume":"77","author":"Lakoff","year":"1980","journal-title":"Conceptual Metaphor in Everyday Language"},{"key":"S135132492200033X_ref30","unstructured":"Li, D. , Wei, F. , Tan, C. , Tang, D. and Ke, X. (2014). Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 49\u201354."},{"key":"S135132492200033X_ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1076"},{"key":"S135132492200033X_ref49","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00049"},{"key":"S135132492200033X_ref52","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1161"},{"key":"S135132492200033X_ref45","unstructured":"Nastase, V. , Judea, A. , Markert, K. and Strube, M. (2012). Local and global context for supervised and unsupervised metonymy resolution. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 183\u2013193."},{"key":"S135132492200033X_ref62","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1458"},{"key":"S135132492200033X_ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.270"},{"key":"S135132492200033X_ref21","doi-asserted-by":"publisher","DOI":"10.3115\/1667583.1667680"},{"key":"S135132492200033X_ref67","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1226"},{"key":"S135132492200033X_ref1","unstructured":"Bahdanau, D. , Cho, K. and Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations."},{"key":"S135132492200033X_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"S135132492200033X_ref2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1023"},{"key":"S135132492200033X_ref17","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"S135132492200033X_ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2012.06.008"},{"key":"S135132492200033X_ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-019-09475-3"},{"key":"S135132492200033X_ref4","unstructured":"Chan, Y.S. and Roth, D. (2011). Exploiting syntactico-semantic structures for relation extraction. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA. Association for Computational Linguistics, pp. 551\u2013560."},{"key":"S135132492200033X_ref42","unstructured":"Mikolov, T. , Sutskever, I. , Chen, K. , Corrado, G.S. and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pp. 3111\u20133119."},{"key":"S135132492200033X_ref51","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"S135132492200033X_ref71","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1139"},{"key":"S135132492200033X_ref23","doi-asserted-by":"publisher","DOI":"10.3115\/981967.982015"},{"key":"S135132492200033X_ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl616"},{"key":"S135132492200033X_ref28","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139173865.013"},{"key":"S135132492200033X_ref19","doi-asserted-by":"publisher","DOI":"10.1515\/cogl.2011.014"},{"key":"S135132492200033X_ref53","doi-asserted-by":"publisher","DOI":"10.1111\/cogs.12341"},{"key":"S135132492200033X_ref6","unstructured":"Devlin, J. , Chang, M.-W. , Lee, K. and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp. 4171\u20134186."},{"key":"S135132492200033X_ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1282"},{"key":"S135132492200033X_ref36","doi-asserted-by":"publisher","DOI":"10.3115\/1118693.1118720"},{"key":"S135132492200033X_ref61","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2012-65"},{"key":"S135132492200033X_ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2016.07.017"},{"key":"S135132492200033X_ref37","doi-asserted-by":"publisher","DOI":"10.3115\/1621474.1621481"},{"key":"S135132492200033X_ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331341"},{"key":"S135132492200033X_ref34","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-2047"},{"key":"S135132492200033X_ref24","unstructured":"Kipf, T.N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations."},{"key":"S135132492200033X_ref59","unstructured":"Socher, R. , Perelygin, A. , Wu, J. , Chuang, J. , Manning, C.D. , Ng, A.Y. and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 1631\u20131642."},{"key":"S135132492200033X_ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1115"},{"key":"S135132492200033X_ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358119"},{"key":"S135132492200033X_ref69","doi-asserted-by":"publisher","DOI":"10.3115\/1220835.1220872"},{"key":"S135132492200033X_ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1136"},{"key":"S135132492200033X_ref39","unstructured":"Mathews, K.A. and Strube, M. (2020). A large harvested corpus of location metonymy. In Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France. European Language Resources Association, pp. 5678\u20135687."},{"key":"S135132492200033X_ref7","doi-asserted-by":"publisher","DOI":"10.3115\/1621474.1621507"},{"key":"S135132492200033X_ref46","doi-asserted-by":"publisher","DOI":"10.3115\/1699571.1699631"}],"container-title":["Natural Language Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S135132492200033X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T07:31:30Z","timestamp":1684481490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S135132492200033X\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":71,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["S135132492200033X"],"URL":"https:\/\/doi.org\/10.1017\/s135132492200033x","relation":{},"ISSN":["1351-3249","1469-8110"],"issn-type":[{"type":"print","value":"1351-3249"},{"type":"electronic","value":"1469-8110"}],"subject":[],"published":{"date-parts":[[2022,8,1]]},"assertion":[{"value":"\u00a9 The Author(s), 2022. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}}]}}