Abstract
Head finder algorithms are used by supervised parsers during their training phase to transform phrase structure trees into dependency ones. For the same phrase structure tree, different head finders produce different dependency trees. Head finders usually have been inspired on linguistic bases and they have been used by parsers as such. In this paper, we present an optimization set-up that tries to produce a head finder algorithm that is optimal for parsing. We also present a series of experiments with random head finders. We conclude that, although we obtain some statistically significant improvements using the optimal head finder, the experiments with random head finders show that random changes in head finder algorithms do not impact dramatically the performance of parsers.
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References
Magerman, D.M.: Natural language parsing as statistical pattern recognition. Ph.D. thesis, Stanford University (1994)
Charniak, E.: A maximum-entropy-inspired parser. In: NAACL 2000 (2000)
Klein, D., Manning, C.: Accurate unlexicalized parsing. In: Proc. 41st ACL (2003)
Bikel, D.: On the Parameter Space of Generative Lexicalized Statistical Parsing Models. PhD thesis, University of Pennsylvania (2004)
Collins, M.: Three generative, lexicalized models for statistical parsing. In: ACL 1997 (1997)
Eisner, J.: Bilexical grammars and a cubictime probabilistic parser. In: Proceedings of IWPT04 (1994)
Klein, D., Manning, C.: Distributional phrase structure induction. In: CoNLL 2001 (2001)
Nivre, J.A.A.: Maltparser: A language-independent system for data-driven dependency parsing. In: Natural Language Engineering, pp. 95–135 (2007)
Marcus, M., Santorini, B.: Building a large annotated corpus of English: The Penn treebank. Computational Linguistics 19, 313–330 (1993)
Thollard, F., Dupont, P., de la Higuera, C.: Probabilistic DFA inference using Kullback-Leibler divergence and minimality. In: Proc. ICML, Stanford (2000)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley, Chichester (1997)
Chiang, D., Recovering, D.B.: latent information in treebanks. In: COLING 2002, Taipei, Taiwan (2002)
Sangati, F., Zuidema, W.: Unsupervised methods for head assignments. In: EACL, pp. 701–709 (2009)
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Domínguez, M.A., Infante-Lopez, G. (2010). Head Finders Inspection: An Unsupervised Optimization Approach. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds) Advances in Natural Language Processing. NLP 2010. Lecture Notes in Computer Science(), vol 6233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14770-8_16
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DOI: https://doi.org/10.1007/978-3-642-14770-8_16
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