{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:46:36Z","timestamp":1792788396881,"version":"4.5.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Economic and Social Research Council","award":["ES\/J500033\/1"],"award-info":[{"award-number":["ES\/J500033\/1"]}],"id":[{"id":"https:\/\/ror.org\/03n0ht308","id-type":"ROR","asserted-by":"crossref"}]},{"name":"European Research Council","award":["648909"],"award-info":[{"award-number":["648909"]}],"id":[{"id":"https:\/\/ror.org\/0472cxd90","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Medical Research Council","award":["MR\/M013049\/1"],"award-info":[{"award-number":["MR\/M013049\/1"]}]},{"name":"Defense Advanced Research Projects Agency","award":["DARPA 15-18-CwC-FP-032"],"award-info":[{"award-number":["DARPA 15-18-CwC-FP-032"]}],"id":[{"id":"https:\/\/ror.org\/02caytj08","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Biomed Semant"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13326-018-0193-x","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T08:02:56Z","timestamp":1547798576000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A neural classification method for supporting the creation of BioVerbNet"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-3249","authenticated-orcid":false,"given":"Billy","family":"Chiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Majewska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sampo","family":"Pyysalo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Wey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulla","family":"Stenius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Korhonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martha","family":"Palmer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"issue":"3","key":"193_CR1","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3758\/BF03195588","volume":"36","author":"DL Nelson","year":"2004","unstructured":"Nelson DL, McEvoy CL, Schreiber TA. The university of south florida free association, rhyme, and word fragment norms. Behav Res Methods Instrum Comput. 2004; 36(3):402\u20137.","journal-title":"Behav Res Methods Instrum Comput"},{"key":"193_CR2","volume-title":"Text Mining for Biology and Biomedicine","author":"S Ananiadou","year":"2006","unstructured":"Ananiadou S, Mcnaught J. Text Mining for Biology and Biomedicine. Norwood: Citeseer; 2006."},{"key":"193_CR3","volume-title":"International Conference on Intelligent Text Processing and Computational Linguistics","author":"G Venturi","year":"2009","unstructured":"Venturi G, Montemagni S, Marchi S, Sasaki Y, Thompson P, McNaught J, Ananiadou S. Bootstrapping a verb lexicon for biomedical information extraction. In: International Conference on Intelligent Text Processing and Computational Linguistics. Berlin: Springer: 2009. p. 137\u201348."},{"key":"193_CR4","volume-title":"Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)","author":"H Tan","year":"2014","unstructured":"Tan H. A system for building framenet-like corpus for the biomedical domain. In: Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi). Gothenburg: Association for Computational Linguistics: 2014. p. 46\u201353."},{"key":"193_CR5","volume-title":"Proceedings of the 9th Global WordNet Conference","author":"A Mondal","year":"2017","unstructured":"Mondal A, Das D, Cambria E, Bandyopadhyay S. Wme 3.0: An enhanced and validated lexicon of medical concepts. In: Proceedings of the 9th Global WordNet Conference. Singapore: Global WordNet Association: 2017."},{"issue":"1","key":"193_CR6","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s12859-018-2039-z","volume":"19","author":"B Chiu","year":"2018","unstructured":"Chiu B, Pyysalo S, Vuli\u0107 I, Korhonen A. Bio-simverb and bio-simlex: wide-coverage evaluation sets of word similarity in biomedicine. BMC Bioinformatics. 2018; 19(1):33.","journal-title":"BMC Bioinformatics"},{"key":"193_CR7","unstructured":"Schuler KK. Verbnet: A broad-coverage, comprehensive verb lexicon. 2005."},{"key":"193_CR8","volume-title":"Proceedings of the Ninth International Conference on Computational Semantics","author":"SW Brown","year":"2011","unstructured":"Brown SW, Dligach D, Palmer M. Verbnet class assignment as a wsd task. In: Proceedings of the Ninth International Conference on Computational Semantics. Oxford: Association for Computational Linguistics: 2011. p. 85\u201394."},{"key":"193_CR9","volume-title":"Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics","author":"A-M Giuglea","year":"2006","unstructured":"Giuglea A-M, Moschitti A. Semantic role labeling via framenet, verbnet and propbank. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Sydney: Association for Computational Linguistics: 2006. p. 929\u201336."},{"key":"193_CR10","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-540-30586-6_9","volume":"3406","author":"L Shi","year":"2005","unstructured":"Shi L, Mihalcea R. Putting pieces together: Combining framenet, verbnet and wordnet for robust semantic parsing. Comput Linguist Intell Text Process. 2005; 3406:100\u2013111.","journal-title":"Comput Linguist Intell Text Process"},{"key":"193_CR11","volume-title":"Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning","author":"M Schmitz","year":"2012","unstructured":"Schmitz M, Bart R, Soderland S, Etzioni O, et al. Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Jeju Island: Association for Computational Linguistics: 2012. p. 523\u201334."},{"issue":"2","key":"193_CR12","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.jbi.2012.12.001","volume":"46","author":"T Lippincott","year":"2013","unstructured":"Lippincott T, Rimell L, Verspoor K, Korhonen A. Approaches to verb subcategorization for biomedicine. J Biomed Inform. 2013; 46(2):212\u201327.","journal-title":"J Biomed Inform"},{"issue":"2","key":"193_CR13","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.jbi.2013.01.001","volume":"46","author":"L Rimell","year":"2013","unstructured":"Rimell L, Lippincott T, Verspoor K, Johnson HL, Korhonen A. Acquisition and evaluation of verb subcategorization resources for biomedicine. J Biomed Inform. 2013; 46(2):228\u201337.","journal-title":"J Biomed Inform"},{"key":"193_CR14","volume-title":"Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II","author":"L Sun","year":"2008","unstructured":"Sun L, Korhonen A, Krymolowski Y. Automatic classification of english verbs using rich syntactic features. In: Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II. Hyderabad: Association for Computational Linguistics: 2008."},{"key":"193_CR15","volume-title":"Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics","author":"A Korhonen","year":"2006","unstructured":"Korhonen A, Krymolowski Y, Collier N. Automatic classification of verbs in biomedical texts. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Sydney: Association for Computational Linguistics: 2006. p. 345\u201352."},{"key":"193_CR16","unstructured":"Machine Learning and AI Via Brain Simulations. https:\/\/forum.stanford.edu\/events\/2011\/2011slides\/plenary\/2011plenaryNg.pdf. Accessed 12 July 2018."},{"key":"193_CR17","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. J Mach Learn Res. 2003; 3:1137\u20131155.","journal-title":"J Mach Learn Res"},{"key":"193_CR18","volume-title":"Proceedings of NIPS","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS. Lake Tahoe: Neural Information Processing Systems: 2013. p. 3111\u20139."},{"key":"193_CR19","volume-title":"Proceedings of EMNLP, vol. 14","author":"J Pennington","year":"2014","unstructured":"Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proceedings of EMNLP, vol. 14. Doha: Association for Computational Linguistics: 2014. p. 1532\u201343."},{"issue":"1","key":"193_CR20","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/s12859-017-1776-8","volume":"18","author":"G Crichton","year":"2017","unstructured":"Crichton G, Pyysalo S, Chiu B, Korhonen A. A neural network multi-task learning approach to biomedical named entity recognition. BMC Bioinformatics. 2017; 18(1):368.","journal-title":"BMC Bioinformatics"},{"key":"193_CR21","volume-title":"Proceedings of the 15th Workshop on Biomedical Natural Language Processing","author":"B Chiu","year":"2016","unstructured":"Chiu B, Crichton G, Korhonen A, Pyysalo S. How to train good word embeddings for biomedical NLP. ACL 2016. In: Proceedings of the 15th Workshop on Biomedical Natural Language Processing. Berlin: Association for Computational Linguistics: 2016. p. 166."},{"key":"193_CR22","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics","author":"GC Marek Rei","year":"2016","unstructured":"Marek Rei GC, Pyysalo S. Attending to characters in neural sequence labeling models. In: Proceedings of the 26th International Conference on Computational Linguistics. Osaka: COLING: 2016."},{"key":"193_CR23","unstructured":"Baker S, Korhonen A, Pyysalo S. Cancer hallmark text classification using convolutional neural networks. BioTxtM 2016. In: Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining, Association for Computational Linguistics. Osaka: 2016. p. 1."},{"issue":"2","key":"193_CR24","doi-asserted-by":"publisher","first-page":"149","DOI":"10.18178\/ijmlc.2016.6.2.590","volume":"6","author":"RD Sharma","year":"2016","unstructured":"Sharma RD, Tripathi S, Sahu SK, Mittal S, Anand A. Predicting online doctor ratings from user reviews using convolutional neural networks. Int J Mach Learn Comput. 2016; 6(2):149.","journal-title":"Int J Mach Learn Comput"},{"key":"193_CR25","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"I Vuli\u0107","year":"2017","unstructured":"Vuli\u0107 I, Mrk\u0161i\u0107 N, Korhonen A. Cross-lingual induction and transfer of verb classes based on word vector space specialisation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics: 2017. p. 2536\u201348."},{"key":"193_CR26","volume-title":"Proceedings of CoNLL","author":"I Vuli\u0107","year":"2017","unstructured":"Vuli\u0107 I, Schwartz R, Rappoport A, Reichart R, Korhonen A. Automatic selection of context configurations for improved class-specific word representations. In: Proceedings of CoNLL. Vancouver: Association for Computational Linguistics: 2017. p. 112\u201322."},{"key":"193_CR27","unstructured":"The Pubmed Central Open Access Subset. 2017. http:\/\/www.pubmedcentral.nih.gov\/about\/openftlist.html. Accessed 5 Sept 2017."},{"key":"193_CR28","unstructured":"Broad Subject Terms. https:\/\/wwwcf.nlm.nih.gov\/serials\/journals\/index.cfm. Accessed 5 Sept 2017."},{"key":"193_CR29","unstructured":"VerbNet Statistics. https:\/\/verbs.colorado.edu\/verb-index\/vn3.3\/. Accessed 10 June 2018."},{"key":"193_CR30","volume-title":"Proceedings of the Annual Symposium on Computer Application in Medical Care","author":"AT McCray","year":"1994","unstructured":"McCray AT, Srinivasan S, Browne AC. Lexical methods for managing variation in biomedical terminologies. In: Proceedings of the Annual Symposium on Computer Application in Medical Care. Bethesda: American Medical Informatics Association: 1994. p. 235."},{"issue":"1","key":"193_CR31","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1186\/1471-2105-12-397","volume":"12","author":"P Thompson","year":"2011","unstructured":"Thompson P, McNaught J, Montemagni S, Calzolari N, Del Gratta R, Lee V, Marchi S, Monachini M, Pezik P, Quochi V, et al. The biolexicon: a large-scale terminological resource for biomedical text mining. BMC Bioinformatics. 2011; 12(1):397.","journal-title":"BMC Bioinformatics"},{"key":"193_CR32","volume-title":"Proceedings of the Workshop on Geometrical Models of Natural Language Semantics","author":"A Vlachos","year":"2009","unstructured":"Vlachos A, Korhonen A, Ghahramani Z. Unsupervised and constrained dirichlet process mixture models for verb clustering. In: Proceedings of the Workshop on Geometrical Models of Natural Language Semantics. Singapore: Association for Computational Linguistics: 2009. p. 74\u201382."},{"issue":"3","key":"193_CR33","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1017\/S135132490600444X","volume":"14","author":"E Joanis","year":"2008","unstructured":"Joanis E, Stevenson S, James D. A general feature space for automatic verb classification. Nat Lang Eng. 2008; 14(3):337\u201367.","journal-title":"Nat Lang Eng"},{"key":"193_CR34","volume-title":"Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1","author":"D \u00d3 S\u00e9aghdha","year":"2008","unstructured":"\u00d3 S\u00e9aghdha D, Copestake A. Semantic classification with distributional kernels. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Manchester: Association for Computational Linguistics: 2008. p. 649\u201356."},{"key":"193_CR35","volume-title":"Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics","author":"L Barak","year":"2014","unstructured":"Barak L, Fazly A, Stevenson S. Learning verb classes in an incremental model. In: Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics. Baltimore: Association for Computational Linguistics: 2014. p. 37\u201345."},{"key":"193_CR36","volume-title":"Proceedings of the 22nd International Conference on Computational Linguistics Volume 1","author":"A Korhonen","year":"2008","unstructured":"Korhonen A, Krymolowski Y, Collier N. The choice of features for classification of verbs in biomedical texts. In: Proceedings of the 22nd International Conference on Computational Linguistics Volume 1. Manchester: Association for Computational Linguistics: 2008. p. 449\u2013456."},{"key":"193_CR37","unstructured":"Sun L. Automatic induction of verb classes using clustering. PhD thesis. 2013."},{"key":"193_CR38","unstructured":"Li J, Brew C. Which are the best features for automatic verb classification. Proceedings of ACL-08: HLT. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. vol. 1. Columbus: 2008. p. 434\u2013442."},{"key":"193_CR39","volume-title":"Proceedings of the ESSLLI 2017 Student Session","author":"D S\u00e4rg","year":"2017","unstructured":"S\u00e4rg D. Hierarchical clustering of estonian verb constructions. ESSLLI 2017 Student Session. In: Proceedings of the ESSLLI 2017 Student Session. Toulouse: European Summer School in Logic, Language & Information: 2017. p. 221\u2013231."},{"key":"193_CR40","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"W Roberts","year":"2014","unstructured":"Roberts W, Egg M. A comparison of selectional preference models for automatic verb classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics: 2014. p. 511\u201322."},{"key":"193_CR41","volume-title":"Proceedings of the 43rd Meeting of the North East Linguistic Society, vol. 43","author":"AS White","year":"2014","unstructured":"White AS, Dudley R, Hacquard V, Lidz J. Discovering classes of attitude verbs using subcategorization frame distributions. In: Proceedings of the 43rd Meeting of the North East Linguistic Society, vol. 43. New York: North East Linguistic Society: 2014."},{"key":"193_CR42","volume-title":"International Conference on Intelligent Text Processing and Computational Linguistics","author":"L Sun","year":"2008","unstructured":"Sun L, Korhonen A, Krymolowski Y. Verb class discovery from rich syntactic data. In: International Conference on Intelligent Text Processing and Computational Linguistics. Berlin: Springer: 2008. p. 16\u201327."},{"key":"193_CR43","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1","author":"S Sahu","year":"2016","unstructured":"Sahu S, Anand A. Recurrent neural network models for disease name recognition using domain invariant features. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1. Berlin: Association for Computational Linguistics: 2016. p. 2216\u201325."},{"key":"193_CR44","volume-title":"Proceedings of the 16th Workshop on Biomedical Natural Language Processing, vol 1","author":"RV Patchigolla","year":"2017","unstructured":"Patchigolla RV, Sahu S, Anand A. Biomedical event trigger identification using bidirectional recurrent neural network based models. BioNLP 2017. In: Proceedings of the 16th Workshop on Biomedical Natural Language Processing, vol 1. Vancouver: Association for Computational Linguistics: 2017. p. 316\u2013321."},{"key":"193_CR45","unstructured":"Bj\u00f6rne J, Airola A, Pahikkala T, Salakoski T. Drug-drug interaction extraction from biomedical texts with svm and rls classifiers. Proceedings of DDIExtraction-2011 challenge task. Huelva: 2011. p. 35\u201342."},{"key":"193_CR46","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: Proceedings of ICLR: 2013."},{"key":"193_CR47","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez J, Guti\u00e9rrez Y, G\u00f3mez JM, Mart\u0131nez-Barco P. Gplsi: Supervised sentiment analysis in twitter using skipgrams. In: Proceedings of SemEval: 2014. p. 294\u20139.","DOI":"10.3115\/v1\/S14-2048"},{"key":"193_CR48","volume-title":"CoNLL, vol. 2015","author":"R Schwartz","year":"2015","unstructured":"Schwartz R, Reichart R, Rappoport A. Symmetric pattern based word embeddings for improved word similarity prediction. In: CoNLL, vol. 2015. Beijing: Association for Computational Linguistics: 2015. p. 258\u201367."},{"key":"193_CR49","volume-title":"ACL (2)","author":"O Levy","year":"2014","unstructured":"Levy O, Goldberg Y. Dependency-based word embeddings. In: ACL (2). Baltimore: Citeseer: 2014. p. 302\u20138."},{"key":"193_CR50","doi-asserted-by":"crossref","unstructured":"De Marneffe M-C, Manning CD. Stanford typed dependencies manual. Technical report, Technical report, Stanford University. 2008.","DOI":"10.3115\/1608858.1608859"},{"key":"193_CR51","unstructured":"Pearl J. Heuristics: intelligent search strategies for computer problem solving. 1984."},{"key":"193_CR52","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018","author":"O Majewska","year":"2018","unstructured":"Majewska O, McCarthy D, Vuli\u0107 I, Korhonen A. Acquiring verb classes through bottom-up semantic verb clustering. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018. Miyazaki: European Language Resources Association: 2018."},{"key":"193_CR53","doi-asserted-by":"crossref","unstructured":"Hakala K, Kaewphan S, Salakoski T, Ginter F. Syntactic analyses and named entity recognition for pubmed and pubmed central\u2014up-to-the-minute. ACL 2016. Berlin: Association for Computational Linguistics: 2016. p. 102.","DOI":"10.18653\/v1\/W16-2913"},{"key":"193_CR54","volume-title":"Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics","author":"E Charniak","year":"2005","unstructured":"Charniak E, Johnson M. Coarse-to-fine n-best parsing and maxent discriminative reranking. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Ann Arbor: Association for Computational Linguistics: 2005. p. 173\u201380."},{"key":"193_CR55","unstructured":"McClosky D. Any domain parsing: automatic domain adaptation for natural language parsing. PhD thesis. 2010."},{"key":"193_CR56","volume-title":"Proceedings of ACL","author":"I Vuli\u0107","year":"2016","unstructured":"Vuli\u0107 I, Korhonen A. Is \u201cuniversal syntax\u201d universally useful for learning distributed word representations? In: Proceedings of ACL. Berlin: Association for Computational Linguistics: 2016. p. 518\u201324."},{"key":"193_CR57","volume-title":"English Verb Classes and Alternations: A Preliminary Investigation","author":"B Levin","year":"1993","unstructured":"Levin B. English Verb Classes and Alternations: A Preliminary Investigation. Chicago: University of Chicago press; 1993."},{"issue":"1","key":"193_CR58","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10579-007-9048-2","volume":"42","author":"K Kipper","year":"2008","unstructured":"Kipper K, Korhonen A, Ryant N, Palmer M. A large-scale classification of english verbs. Lang Resour Eval. 2008; 42(1):21\u201340.","journal-title":"Lang Resour Eval"},{"issue":"1","key":"193_CR59","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1002\/asi.20737","volume":"59","author":"J-S Kuo","year":"2008","unstructured":"Kuo J-S, Li H, Yang Y-K. Active learning for constructing transliteration lexicons from the web. J Assoc Inf Sci Technol. 2008; 59(1):126\u201335.","journal-title":"J Assoc Inf Sci Technol"},{"key":"193_CR60","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018","author":"AC Gyllensten","year":"2018","unstructured":"Gyllensten AC, Sahlgren M. Distributional term set expansion. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018. Miyazaki: European Language Resources Association: 2018."},{"key":"193_CR61","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T. Enriching word vectors with subword information. Trans Assoc Comput Linguist. 2017; 5:135\u201346.","journal-title":"Trans Assoc Comput Linguist"}],"container-title":["Journal of Biomedical Semantics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-018-0193-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13326-018-0193-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-018-0193-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:21:11Z","timestamp":1790036471000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13326-018-0193-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,18]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["193"],"URL":"https:\/\/doi.org\/10.1186\/s13326-018-0193-x","relation":{},"ISSN":["2041-1480"],"issn-type":[{"value":"2041-1480","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,18]]},"assertion":[{"value":"3 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors have obtained consent for publication from all participants in this study.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","label":"Publisher\u2019s Note","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"2"}}