{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:25:44Z","timestamp":1777703144420,"version":"3.51.4"},"reference-count":42,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2016,6,23]],"date-time":"2016-06-23T00:00:00Z","timestamp":1466640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>As a fundamental task of natural language processing, semantic role labeling (SRL) have attracted much attention of researchers in recent years. However, with increasing features being added into the studies, the performance growth trend of SRL is gradually slowing down. So new ways must be found to improve the performance of semantic analysis. Word sense information is useful for SRL task. But how to effectively make use of word sense information is a key issue. Referring to synergetics, we can regard semantic analysis process as competitive process of many semantics order parameters under coherent action and interactive collaboration of semantic role-related features and word sense-related features. Accordingly, we propose a semantic role labeling model with word sense information based on improved synergetic neural network (SNN). Our contributions are three-fold. Firstly, role-related features and word sense-related features are used to configure semantic order parameters of SNN. Secondly, network parameters are reconstructed which can reflect the relationship of driving and restraining each other between various linguistic features. Finally, we use an improved quantum particle swarm algorithm (QPSO) to realize the optimization of network parameter which has stronger search ability and faster convergence speed. By evaluating our model on the OntoNotes 2.0 corpus, the experiment results show the proposed model in this paper leads to a higher performance for SRL.<\/jats:p>","DOI":"10.3233\/jifs-15947","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T10:27:03Z","timestamp":1471602423000},"page":"1469-1480","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["An improving SRL model with word sense information using an improved synergetic neural network model"],"prefix":"10.1177","volume":"31","author":[{"given":"Zhehuang","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mathematics Sciences, Huaqiao University, Quanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yidong","family":"Chen","sequence":"additional","affiliation":[{"name":"Cognitive Science Department, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,6,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1162\/coli.2008.34.2.257"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/coli.2008.34.2.161"},{"key":"e_1_3_2_4_2","first-page":"218","article-title":"Can semantic role labeling improve SMT?","author":"Wu D.","year":"2009","unstructured":"WuD. and PascaleF., Can semantic role labeling improve SMT?In Proceedings of EAMT, 2009, pp. 218\u2013225.","journal-title":"In Proceedings of EAMT"},{"key":"e_1_3_2_5_2","first-page":"12","article-title":"Using semantic roles to improve question answering","author":"Shen D.","year":"2007","unstructured":"ShenD. and LapataM., Using semantic roles to improve question answering, In Proceedings of EMNLP, 2007, pp. 12\u201321.","journal-title":"In Proceedings of EMNLP"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3115\/1220355.1220455"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3115\/1075096.1075098"},{"key":"e_1_3_2_8_2","first-page":"159","article-title":"The CoNLL-shared task on joint parsing of syntactic and semantic dependencies","author":"Surdeanu M.","year":"2008","unstructured":"SurdeanuM., JohanssonR., MeyersA., MarquezL. and NivreJ., The CoNLL-shared task on joint parsing of syntactic and semantic dependencies, Twelfth Conference on Computational Natural Language Learning, 2008, pp. 159\u2013177.","journal-title":"Twelfth Conference on Computational Natural Language Learning"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620777"},{"key":"e_1_3_2_10_2","first-page":"246","article-title":"Improving semantic role labeling with word sense","author":"Che W.X.","year":"2010","unstructured":"CheW.X., LiuT. and LiY.Q., Improving semantic role labeling with word sense, Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2010, pp. 246\u2013249.","journal-title":"Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.01.022"},{"issue":"1","key":"e_1_3_2_12_2","first-page":"203","article-title":"Integrative semantic dependency parsing via efficient large-scale feature selection","volume":"46","author":"Zhao H.","year":"2014","unstructured":"ZhaoH., ZhangX. and KitC., Integrative semantic dependency parsing via efficient large-scale feature selection, Journal of Artificial Intelligence Research46(1) (2014), 203\u2013233.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1001.2011.03885"},{"key":"e_1_3_2_14_2","first-page":"80","article-title":"Mixing weak learners in semantic parsin","author":"Nielsen R.D.","year":"2004","unstructured":"NielsenR.D. and PradhanS., Mixing weak learners in semantic parsin, In Proceedings of EMNLP-2004, 2004, pp. 80\u201387.","journal-title":"In Proceedings of EMNLP-2004"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3115\/1706543.1706579"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"HakenH. 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