nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Multilingual Unsupervised Dependency Parsing with Unsupervised POS Tags

verfasst von : David Mareček

Erschienen in: Advances in Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we present experiments with unsupervised dependency parser without using any part-of-speech tags learned from manually annotated data. We use only unsupervised word-classes and therefore propose fully unsupervised approach of sentence structure induction from a raw text. We show that the results are not much worse than the results with supervised part-of-speech tags.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Low-Level Features for Paraphrase Identification

Nächstes Kapitel Term Dependence Statistical Measures for Information Retrieval Tasks

The software of the unsupervised parser described in [10] can be downloaded at http://ufal.mff.cuni.cz/udp.

We can do the inference and evaluation on the same data, since the correct annotation (labels and dependencies) is not used in the inference (unsupervised training).

The Clark’s tool for unsupervised POS induction can be downloaded at http://www.cs.rhul.ac.uk/home/alexc/pos2.tar.gz.

Blunsom, P., Cohn, T.: A hierarchical Pitman-Yor process hmm for unsupervised part of speech induction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 865–874. Association for Computational Linguistics, Stroudsburg (2011). http://dl.acm.org/citation.cfm?id=2002472.2002582

Brown, P.F., deSouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992). http://dl.acm.org/citation.cfm?id=176313.176316

Clark, A.: Combining distributional and morphological information for part of speech induction. In: Proceedings of 10th EACL, pp. 59–66 (2003)

Ganchev, K., Gillenwater, J., Taskar, B.: Dependency grammar induction via bitext projection constraints. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL 2009, vol. 1, pp. 369–377. Association for Computational Linguistics, Stroudsburg (2009). http://dl.acm.org/citation.cfm?id=1687878.1687931

Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Interdisciplinary Statistics. Chapman & Hall, London (1996)

Headden III, W.P., Johnson, M., McClosky, D.: Improving unsupervised dependency parsing with richer contexts and smoothing. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2009, pp. 101–109. Association for Computational Linguistics, Stroudsburg (2009)

Klein, D., Manning, C.D.: Corpus-based induction of syntactic structure: models of dependency and constituency. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL 2004. Association for Computational Linguistics, Stroudsburg (2004)

Majliš, M., Žabokrtský, Z.: Language richness of the web. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012). European Language Resources Association (ELRA), Istanbul, Turkey, May 2012

Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: the penn treebank. Comput. Linguist. 19(2), 313–330 (1994)

10.

Mareček, D., Straka, M.: Stop-probability estimates computed on a large corpus improve unsupervised dependency parsing. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1 (Long Papers), pp. 281–290. Association for Computational Linguistics, Sofia, Bulgaria, August 2013

11.

Mareček, D., Žabokrtský, Z.: Gibbs sampling with treeness constraint in unsupervised dependency parsing. In: Proceedings of RANLP Workshop on Robust Unsupervised and Semisupervised Methods in Natural Language Processing, pp. 1–8. Hissar, Bulgaria (2011)

12.

Mareček, D., Žabokrtský, Z.: Exploiting reducibility in unsupervised dependency parsing. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, pp. 297–307. Association for Computational Linguistics, Stroudsburg (2012)

13.

McDonald, R., Petrov, S., Hall, K.: Multi-source transfer of delexicalized dependency parsers. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 62–72. Association for Computational Linguistics, Stroudsburg, July 2011. http://dl.acm.org/citation.cfm?id=2145432.2145440

14.

Petrov, S., Das, D., McDonald, R.: A universal part-of-speech tagset. In: Chair, N.C.C., Choukri, K., Declerck, T., Doan, 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

15.

Rasooli, M.S., Faili, H.: Fast unsupervised dependency parsing with arc-standard transitions. In: Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, ROBUS-UNSUP 2012, pp. 1–9. Association for Computational Linguistics, Stroudsburg (2012)

16.

Seginer, Y.: Fast unsupervised incremental parsing. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 384–391. Association for Computational Linguistics, Prague, Czech Republic (2007)

17.

Spitkovsky, V.I., Alshawi, H., Chang, A.X., Jurafsky, D.: Unsupervised dependency parsing without gold part-of-speech tags. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011) (2011)

18.

Spitkovsky, V.I., Alshawi, H., Jurafsky, D.: Punctuation: making a point in unsupervised dependency parsing. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL-2011) (2011)

19.

Spitkovsky, V.I., Alshawi, H., Jurafsky, D.: Three dependency-and-boundary models for grammar induction. In: Proceedings of the 2012 Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012) (2012)

20.

Spitkovsky, V.I., Alshawi, H., Jurafsky, D.: Breaking out of local optima with count transforms and model recombination: a study in grammar induction. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1983–1995. Association for Computational Linguistics, Seattle, October 2013

21.

Zeman, D., Mareček, D., Popel, M., Ramasamy, L., Štěpánek, J., Žabokrtský, Z., Hajič, J.: HamleDT: to parse or not to parse? In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012). European Language Resources Association (ELRA), Istanbul, Turkey (2012)

Titel: Multilingual Unsupervised Dependency Parsing with Unsupervised POS Tags
verfasst von: David Mareček
Verlag: Springer International Publishing
Buch: Advances in Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-27059-3

Electronic ISBN: 978-3-319-27060-9

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-27060-9_6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner