Abstract
The detection and analysis of events in natural language texts plays an important role in several NLP applications such as summarization and question answering. In this study we introduce a machine learning-based approach that can detect and classify verbal and infinitival events in Hungarian texts. First we identify the multiword noun + verb and noun + infinitive expressions. Then the events are detected and the identified events are classified. For each problem, we applied binary classifiers based on rich feature sets. The models were expanded with rule-based methods too. In this study we introduce new methods for this application area. According to our best knowledge ours is the first result for detection and classification of verbal and infinitival events in Hungarian natural language texts. Evaluating them on test databases, our algorithms achieved competitive results as compared to the current English results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bethard, S.J.: Finding Event, Temporal and Causal Structure in Text: A Machine Learning Approach. Computer Science. Boulder, CO, University of Colorado (2002)
Bittar, A.: Annotation of Events and Temporal Expressions in French Texts. In: ACL-IJCNLP 2009 Proceedings of the Third Linguistic Annotation Workshop, pp. 48–51 (2009)
Csendes, D., Csirik, J.A., Gyimóthy, T.: The Szeged Corpus: A POS Tagged and Syntactically Annotated Hungarian Natural Language Corpus. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 41–47. Springer, Heidelberg (2004)
Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing. In: Computational Linguistics, and Speech Recognition. Prentice-Hall, Upper Saddle River (2000)
Kata, G., Héja, E.: Clustering Hungarian verbs on the basis of complementation patterns. In: Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop, pp. 91–96 (2007)
Llorens, H., Saquete, E., Navarro-Colorado, B.: TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 725–733 (2010)
Marsic, G.: Temporal processing of news: annotation of temporal expressions, verbal events and temporal relations. PhD thesis, University of Wolverhampton (2011)
Miháltz, M., Hatvani, C., Kuti, J., Szarvas, G., Csirik, J., Prószéky, G., Váradi, T.: Methods and Results of the Hungarian WordNet Project. In: Tanács, A., Csendes, D., Vincze, V., Fellbaum, C., Vossen, P. (eds.) Proceedings of the Fourth Global WordNet Conference, GWC 2008, pp. 311–320. University of Szeged, Szeged (2008)
Subecz, Z., Nagyné, C.É.: Igei események detektálása és osztályozása magyar nyelvű szövegekben. In: X. Magyar Számítógépes Nyelvészeti Konferencia, Szeged, pp. 237–247 (2014)
Vincze, V.: Félig kompozicionális főnév + ige szerkezetek a Szeged Korpuszban. In: VI. Magyar Számítógépes Nyelvészeti Konferencia, Szeged, pp. 390–393 (2009)
Vincze, V., Zsibrita, J., Nagy, T.I.: Dependency Parsing for Identifying Hungarian Light Verb Constructions. In: Proceedings of International Joint Conference on Natural Language Processing, pp. 207–215 (2013)
Zsibrita, J., Vincze, V., Farkas, R.: Magyarlanc 2.0: szintaktikai elemzés és felgyorsított szófaji egyértelműsítés. In: IX. Magyar Számítógépes Nyelvészeti Konferencia, Szeged, pp. 368–374 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Subecz, Z. (2014). Detection and Classification of Events in Hungarian Natural Language Texts. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2014. Lecture Notes in Computer Science(), vol 8655. Springer, Cham. https://doi.org/10.1007/978-3-319-10816-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-10816-2_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10815-5
Online ISBN: 978-3-319-10816-2
eBook Packages: Computer ScienceComputer Science (R0)