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Erschienen in:
Enabling Medical Translation for Low-Resource Languages Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Using Syntactic and Semantic Features for Classifying Modal Values in the Portuguese Language

verfasst von : João Sequeira, Teresa Gonçalves, Paulo Quaresma, Amália Mendes, Iris Hendrickx

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic taggers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and \(F_1\). Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntactic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achieving (in almost every verb) an improvement in \(F_1\) when compared to the traditional bow approach.

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Vorheriges Kapitel Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish
Nächstes Kapitel Detecting the Likely Causes Behind the Emotion Spikes of Influential Twitter Users
Fußnoten
1
The MMAX2 software is platform-independent, written in java and can freely be downloaded from http://​mmax2.​sourceforge.​net/​.
 
Literatur
1.
der Auwera, J.V., Plungian, V.A.: Modality’s semantic map. Linguist. Typol. 1(2), 79–124 (1998)
2.
Baker, K., Bloodgood, M., Dorr, B., Filardo, N.W., Levin, L., Piatko, C.: A modality Lexicon and its use in automatic tagging. In: Chair, N.C.C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association (ELRA), Valletta, Malta, May 2010
3.
Bick, E.: The Parsing System PALAVRAS. Aarhus University Press, Aarhus (1999)
4.
Diab, M.T., Levin, L.S., Mitamura, T., Rambow, O., Prabhakaran, V., Guo, W.: Committed belief annotation and tagging. In: Third Linguistic Annotation Workshop, pp. 68–73. The Association for Computer Linguistics, Singapore, August 2009
5.
Farkas, R., Vincze, V., Móra, G., Csirik, J., Szarvas, G.: The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 1–12. Association for Computational Linguistics, Uppsala, Sweden, July 2010
6.
Généreux, M., Hendrickx, I., Mendes, A.: Introducing the reference corpus of contemporary Portuguese on-line. In: Calzolari, N., Choukri, K., Declerck, T., Dogan, M.U., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S. (eds.) LREC 2012, pp. 2237–2244. European Language Resources Association (ELRA), Istanbul (2012)
7.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRef
8.
Hendrickx, I., Mendes, A., Mencarelli, S.: Modality in text: a proposal for corpus annotation. In: Chair, N.C.C., Choukri, K., Declerck, T., Doğan, M.U., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012). European Language Resources Association (ELRA), Istanbul, Turkey, May 2012
9.
Matsuyoshi, S., Eguchi, M., Sao, C., Murakami, K., Inui, K., Matsumoto, Y.: Annotating event mentions in text with modality, focus, and source information. In: Chair, N.C.C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association (ELRA), Valletta, Malta, May 2010
10.
McShane, M., Nirenburg, S., Beale, S., O’Hara, T.: Semantically rich human-aided machine annotation. In: Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky, pp. 68–75. Association for Computational Linguistics, Ann Arbor, Michigan, June 2005
11.
Mendes, A., Hendrickx, I., Salgueiro, A., Ávila, L.: Annotating the interaction between focus and modality: the case of exclusive particles. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 228–237. Association for Computational Linguistics, Sofia, Bulgaria, August 2013
12.
Miwa, M., Thompson, P., McNaught, J., Kell, D.B., Ananiadou, S.: Extracting semantically enriched events from biomedical literature. BMC Bioinform. 13, 108 (2012)CrossRef
13.
Müller, C., Strube, M.: Multi-level annotation of linguistic data with MMAX2. In: Braun, S., Kohn, K., Mukherjee, J. (eds.) Corpus Technology and Language Pedagogy: New Resources, New Tools, New Methods, pp. 197–214. Peter Lang, Frankfurt a.M., Germany (2006)
14.
Nirenburg, S., McShane, M.: Annotating modality. Technical report, University of Maryland, Baltimore County, USA, March 2008
15.
Nissim, M., Pietrandrea, P., Sanso, A., Mauri, C.: Cross-linguistic annotation of modality: a data-driven hierarchical model. In: Proceedings of IWCS 2013 WAMM Workshop on the Annotation of Modal Meaning in Natural Language, pp. 7–14. Association for Computational Linguistics, Postam, Germany (2013)
16.
Palmer, F.R.: Mood and Modality. Cambridge Textbooks in Linguistics. Cambridge University Press, Cambridge (1986)
17.
Prabhakaran, V., Bloodgood, M., Diab, M., Dorr, B., Levin, L., Piatko, C.D., Rambow, O., Van Durme, B.: Statistical modality tagging from rule-based annotations and crowdsourcing. In: Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExProM 2012, pp. 57–64. Association for Computational Linguistics, Stroudsburg, PA, USA (2012)
18.
Ruppenhofer, J., Rehbein, I.: Yes we can!? Annotating English modal verbs. In: Chair, N.C.C., Choukri, K., Declerck, T., Doğan, M.U., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S. (eds.) Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012). European Language Resources Association (ELRA), Istanbul, Turkey, May 2012
19.
Sauri, R., Verhagen, M., Pustejovsky, J.: Annotating and recognizing event modality in text. In: FLAIRS Conference, pp. 333–339 (2006)
20.
Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, Hoboken (1998)
21.
Ávila, L., Melo, H.: Challenges in modality annotation in a Brazilian Portuguese spontaneous speech corpus. In: Proceedings of IWCS 2013 WAMM Workshop on the Annotation of Modal Meaning in Natural Language. Association for Computational Linguistics, Postam, Germany (2013)
Metadaten
Titel
Using Syntactic and Semantic Features for Classifying Modal Values in the Portuguese Language
verfasst von
João Sequeira
Teresa Gonçalves
Paulo Quaresma
Amália Mendes
Iris Hendrickx
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75487-1_28

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