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Current Trends in Parsing Technology

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Trends in Parsing Technology

Part of the book series: Text, Speech and Language Technology ((TLTB,volume 43))

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Abstract

Significant advances in natural language processing require the development of adaptive syterns both for spoken and written language: systems that can interact naturally with human users, extend easily to new domains, produce readily usable translations of several languages, search the web rapidly and accurately, surnrnarise news coherently, and detect shifts in moods and emotions. Recent statistical datadriven techniques in natural language processing aim at acquiring the needed adaptivity by modelling the syntactic and lexical properties of large quantities of naturally occurring text.

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References

  • Attardi, G. (2006). Experiments with a multilanguage non-projective dependency parser. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), New York, NY, pp. 166–170.

    Google Scholar 

  • Baldwin, T., M. Dras, J. Hockenmaier, T. Holloway King, and G. van Noord (2007). The impact of deep linguistic processing on parsing technology. In Proceedings of the 10th International Conference on Parsing Technologies, Prague, pp. 36–38.

    Google Scholar 

  • Baldwin, T., M. Dras, J. Hockenmaier, T. H. King, and G. van Noord (Eds.) (2007). ACL 2007 Workshop on Deep Linguistic Processing, Prague.

    Google Scholar 

  • Berwick, R. C. and A. S. Weinberg (1984). The Grammatical Basis of Linguistic Performance. Cambridge, MA: MIT Press.

    Google Scholar 

  • Bikel, D. (2004). Intricacies of Collins’ parsing model. Computational Linguistics 40(4), 479–511.

    Article  Google Scholar 

  • Blitzer, J., R. McDonald, and F. Pereira (2006). Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, pp. 120–128.

    Google Scholar 

  • Bod, R. (2007). Is the end of supervised parsing in sight? In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, pp. 400–407.

    Google Scholar 

  • Buchholz, S. and E. Marsi (2006). CoNLL-X shared task on multilingual dependency parsing. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL’06), New York, NY, pp. 149–164.

    Google Scholar 

  • Bunt, H., J. Carroll, and G. Satta (Eds.) (2004). New Developments in Parsing Technology. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Burkett, D. and D. Klein (2008). Two languages are better than one (for syntactic parsing). In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Honolulu, HI, pp. 877–886.

    Google Scholar 

  • Carroll, J. and S. Oepen (2005). High efficiency realization for a wide-coverage unification grammar. In Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP05), Jeju Island, pp. 165–176.

    Google Scholar 

  • Chiang, D. (2005). A hierarchical phrase-based model for statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, MI, pp. 263–270.

    Google Scholar 

  • Chiang, D. and D. Wu (Eds.) (2008). Proceedings of the ACL-08: HLT 2nd Workshop on Syntax and Structure in Statistical Translation (SSST-2). Columbus, OH: Association for Computational Linguistics.

    Google Scholar 

  • Ciaramita, M. and G. Attardi (2007). Dependency parsing with second-order feature maps and annotated semantic information. In Proceedings of the 10th International Conference on Parsing Technologies, Prague, pp. 133–143.

    Google Scholar 

  • Ciaramita, M., G. Attardi, F. Dell’Orletta, and M. Surdeanu (2008). DeSRL: a linear-time semantic role labeling system. In CoNLL 2008: Proceedings of the 12th Conference on Computational Natural Language Learning, Manchester, pp. 258–262.

    Google Scholar 

  • Collins, M. J. (1999). Head-Driven Statistical Models for Natural Language Parsing. Ph. D. thesis, Department of Computer Science, University of Pennsylvania.

    Google Scholar 

  • Cowan, B., I. Kučerová, and M. Collins (2006). A discriminative model for tree-to-tree translation. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, pp. 232–241.

    Google Scholar 

  • Culotta, A. and J. Sorensen (2004). Dependency tree kernels for relation extraction. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL’04), Barcelona, pp. 423–429.

    Google Scholar 

  • Daumée III, H. and D. Marcu (2006). Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26, 101–126.

    Google Scholar 

  • Ding, Y. and M. Palmer (2004). Synchronous dependency insertion grammars: a grammar formalism for syntax based statistical MT. In Proceedings of the Workshop on Recent Advances in Dependency Grammar, Geneva, pp. 90–97.

    Google Scholar 

  • Dubey, A. and F. Keller (2004). Probabilistic parsing for German using sister-head dependencies. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL’03), Sapporo, pp. 96–103.

    Google Scholar 

  • Ge, R. and R. J. Mooney (2005). A statistical semantic parser that integrates syntax and semantics. In Proceedings of the 9th Conference on Computational Natural Language Learning (CONLL-05), Ann Arbor, MI, pp. 9–16.

    Google Scholar 

  • Gildea, D. (2001). Corpus variation and parser performance. In 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP-2001), Pittsburgh, PA, pp. 167–202.

    Google Scholar 

  • Henderson, J. (2003). Inducing history representations for broad-coverage statistical parsing. In Proceedings of the Joint Meeting of the North American Chapter of the Association for Computational Linguistics and the Human Language Technology Conference (NAACL-HLT’03), Edmonton, pp. 103–110.

    Google Scholar 

  • Henderson, J., P. Merlo, G. Musillo, and I. Titov (2008). A latent variable model of synchronous parsing for syntactic and semantic dependencies. In Proceedings of CONLL 2008, Manchester, pp. 178–182.

    Google Scholar 

  • Hwa, R., P. Resnik, A. Weinberg, C. Cabezas, and O. Kolak (2005). Bootstrapping parsers via syntactic projection across parallel text. Natural Language Engineering 11(3), 311–325.

    Article  Google Scholar 

  • Johnson, M. and S. Riezler (2000). Exploiting auxiliary distributions in stochastic unification-based grammars. In Proceedings of the 1st meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2000), Seattle, WA pp. 154–161.

    Google Scholar 

  • Kate, R. and R. Mooney (2007). Semi-supervised learning for semantic parsing using support vector machines. In Human Language Technologies 2007: the Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers, Rochester, NY, pp. 81–84.

    Google Scholar 

  • Klein, D. (2005). The Unsupervised Learning of Natural Language Structure. Ph. D. thesis, Department of Computer Science, Stanford University.

    Google Scholar 

  • Klein, D. and C. Manning (2004). Corpus-based induction of syntactic structure: models of dependency and constituency. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL’04), Barcelona, pp. 478–485.

    Google Scholar 

  • Klein, D. and C. D. Manning (2003). Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting of the ACL (ACL’03), Sapporo, pp. 423–430.

    Google Scholar 

  • Koo, T., X. Carreras, and M. Collins (2008, June). Simple semi-supervised dependency parsing. In Proceedings of ACL-08: HLT, Columbus, OH, pp. 595–603.

    Google Scholar 

  • Kübler, S., R. McDonald, and J. Nivre (2009). Dependency Parsing. San Francisco, CA: Morgan & Claypool.

    Google Scholar 

  • Marcus, M., B. Santorini, and M. Marcinkiewicz (1993). Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics 19, 313–330.

    Google Scholar 

  • McClosky, D., E. Charniak, and M. Johnson (2006). Reranking and self-training for parser adaptation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, pp. 337–344.

    Google Scholar 

  • McClosky, D., E. Charniak, and M. Johnson (2008). When is self-training effective for parsing? In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, pp. 561–568.

    Google Scholar 

  • McDonald, R., K. Crammer, and F. Pereira (2005). Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, MI, pp. 91–98.

    Google Scholar 

  • Merlo, P. and G. Musillo (2005). Accurate function parsing. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, BC, pp. 620–627.

    Google Scholar 

  • Merlo, P. and G. Musillo (2008). Semantic parsing for high-precision semantic role labelling. In Proceedings of the 12th Conference on Computational Natural Language Learning (CONLL-08), Manchester, pp. 1–8.

    Google Scholar 

  • Meyers, A., R. Reeves, C. Macleod, R. Szekely, V. Zielinska, B. Young, and R. Grishman (2004, May). The NomBank project: an interim report. In A. Meyers (Ed.), HLT-NAACL 2004 Workshop: Frontiers in Corpus Annotation, Boston, MA, pp. 24–31.

    Google Scholar 

  • Miller, S., H. Fox, L. Ramshaw, and R. Weischedel (2000). A novel use of statistical parsing to extract information from text. In Proceedings of the First Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2000), Seattle, WA pp. 226–233.

    Google Scholar 

  • Musillo, G. and P. Merlo (2005). Lexical and structural biases for function parsing. In Proceedings of the 9th International Workshop on Parsing Technology (IWPT’05), Vancouver, BC, pp. 83–92.

    Google Scholar 

  • Musillo, G. and P. Merlo (2008). Unlexicalised hidden variable models of split dependency grammars. In Proceedings of the Annual Conference for Computational Linguistics (ACL’08), Columbus, OH, pp. 213–216.

    Google Scholar 

  • Nivre, J. (2004). Incrementality in deterministic dependency parsing. In Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together (ACL), Barcelona, pp. 50–57.

    Google Scholar 

  • Nivre, J. (2006). Inductive Dependency Parsing. Dordrecht: Springer.

    Book  Google Scholar 

  • Nivre, J. (2008). Sorting out dependency parsing. In Proceedings of GoTAL 2008, Gothenburg, pp. 16–27.

    Google Scholar 

  • Nivre, J., J. Hall, S. Kübler, R. McDonald, J. Nilsson, S. Riedel, and D. Yuret (2007). The CoNLL 2007 shared task on dependency parsing. In Proceedings of the CoNLL Shared Task of EMNLP-CoNLL 2007, Prague, pp. 915–932.

    Google Scholar 

  • Nivre, J., J. Hall, J. Nilsson, A. Chanev, G. Eryiğit, S. Kübler, S. Marinov, and E. Marsi (2007). Maltparser: a language-independent system for data-driven dependency parsing. Journal of Natural Language Engineering 13, 95–135.

    Google Scholar 

  • Nivre, J. and R. McDonald (2008). Integrating graph-based and transition-based dependency parsers. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL’08), Columbus, OH, pp. 950–958.

    Google Scholar 

  • Nivre, J. and B. Megyesi (2007). Bootstrapping a Swedish treeebank using cross-corpus harmonization and annotation projection. In Proceedings of the 6th International Workshop on Treebanks and Linguistic Theories, Bergen, pp. 97–10.

    Google Scholar 

  • Padó, S. (2007). Cross-lingual Annotation Projection Models for Role-Semantic Information. Ph. D. thesis, Saarland University.

    Google Scholar 

  • Padó, S. and M. Lapata (2005). Cross-linguistic projection of role-semantic information. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, BC, pp. 859–866.

    Google Scholar 

  • Palmer, M., D. Gildea, and P. Kingsbury (2005). The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics 31, 71–105.

    Article  Google Scholar 

  • Petrov, S. and D. Klein (2008). Discriminative log-linear grammars with latent variables. In J. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Advances in Neural Information Processing Systems 20 (NIPS), pp. 1153–1160.

    Google Scholar 

  • Pollard, C. and I. A. Sag (1987). Information-Based Syntax and Semantics. Volume 1: Fundamentals. Stanford, CA: CSLI Publications.

    Google Scholar 

  • Pollard, C. and I. A. Sag (1994). Head-Driven Phrase Structure Grammar. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Prescher, D. (2005, October). Head-driven PCFGs with latent-head statistics. In Proceedings of the 9th International Workshop on Parsing Technology (IWPT), Vancouver, BC, pp. 115–124.

    Google Scholar 

  • Quirk, C., A. Menezes, and C. Cherry (2005). Dependency treelet translation: syntactically informed phrasal SMT. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, MI, pp. 271–279.

    Google Scholar 

  • Roark, B. and M. Bacchiani (2003). Supervised and unsupervised PCFG adaptation to novel domains. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2003), Edmonton, AB, pp. 126–133.

    Google Scholar 

  • Sag, I. A., T. Wasow, and E. Bender (2003). Syntactic Theory: a Formal Introduction (2nd ed.). Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Sagae, K. and J. Tsujii (2008). Shift-reduce dependency DAG parsing. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, pp. 753–760.

    Google Scholar 

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

    Google Scholar 

  • Shen, L., J. Xu, and R. Weischedel (2008). A new string-to-dependency machine translation algorithm with a target dependency language model. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT), Columbus, OH, pp. 577–585.

    Google Scholar 

  • Smith, N. A. (2006). Novel Estimation Methods for Unsupervised Discovery of Latent Structure in Natural Language Text. Ph. D. thesis, Johns Hopkins University.

    Google Scholar 

  • Snyder, B. and R. Barzilay (2008). Unsupervised multilingual learning for morphological segmentation. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT), Columbus, OH, pp. 737–745.

    Google Scholar 

  • Snyder, B., T. Naseem, J. Eisenstein, and R. Barzilay (2008). Unsupervised multilingual learning for POS tagging. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP’08), Honolulu, HI, pp. 1041–1050.

    Google Scholar 

  • Surdeanu, M., R. Johansson, A. Meyers, L. Màrquez, and J. Nivre (2008). The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. In Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL-2008), Manchester pp. 159–177.

    Google Scholar 

  • Swier, R. and S. Stevenson (2005). Exploiting a verb lexicon in automatic semantic role labelling. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-05), Vancouver, BC, pp. 883–890.

    Google Scholar 

  • Tesnière, L. (1959). Elments de syntaxe structurale. Paris: Klincksieck.

    Google Scholar 

  • Titov, I. and J. Henderson (2006). Loss minimization in parse reranking. In Proceedings of the Conference on Empirical Methods in Natural Language processing (EMNLP’05), Sydney.

    Google Scholar 

  • Titov, I. and J. Henderson (2007). Constituent parsing with Incremental Sigmoid Belief Networks. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL’07), Prague, pp. 632–639.

    Google Scholar 

  • Titov, I., J. Henderson, P. Merlo, and G. Musillo (2009, July). Online projectivisation for synchronous parsing of semantic and syntactic dependencies. Proceedings of the 21st International Conference on Artificial Intelligence (IJCAI’09), Pasadena, CA.

    Google Scholar 

  • Wang, Q. I., D. Schuurmans, and D. Lin (2008). Semi-supervised convex training for dependency parsing. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT), Columbus, OH, pp. 532–540.

    Google Scholar 

  • Wong, Y. W. and R. Mooney (2007). Learning synchronous grammars for semantic parsing with lambda calculus. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL’07), Prague, pp. 960–967.

    Google Scholar 

  • Wu, D. (1997). Stochastic inversion transduction grammars and bilingual parsing of parallel corpora. Computational Linguistics 23(3), 377–403.

    Google Scholar 

  • Wu, D. and D. Chiang (Eds.) (2007, April). Proceedings of SSST, NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation. Rochester, NY: Association for Computational Linguistics.

    Google Scholar 

  • Yamada, H. and Y. Matsumoto (2003). Statistical dependency analysis with support vector machines. In Proceedings of the 8th International Workshop on Parsing Technologies (IWPT’03), Nancy, pp. 195–206.

    Google Scholar 

  • Zettlemoyer, L. and M. Collins (2007). Online learning of relaxed CCG grammars for parsing to logical form. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, pp. 678–687.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Paola Merlo

  2. Tilburg Center for Cognition and Communication (TiCC) and Department of Communication and Information Sciences, Tilburg University, Tilburg, The Netherlands

    Harry Bunt

  3. Uppsala University, Uppsala, Sweden

    Joakim Nivre

Authors
  1. Paola Merlo
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  2. Harry Bunt
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  3. Joakim Nivre
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Corresponding authors

Correspondence to Paola Merlo , Johan Hall , Ivan Titov , Kenji Sagae , Tetsuji Nakagawa , Massimiliano Ciaramita , Qin Iris Wang , Jason Eisner , Keith Hall , Detlef Prescher , Gertjan van Noord , Pierre Boullier , Yi Zhang , Takashi Ninomiya , Tadayoshi Hara or Rebecca Watson .

Editor information

Editors and Affiliations

  1. Tilburg University, Warandelaan 2, Tilburg, 5000 LE, Netherlands

    Harry Bunt

  2. Dépt. Linguistique, Université de Genève, rue de Candolle 2, Genève, 1211, Switzerland

    Paola Merlo

  3. Pimpstensvägen 16, Uppsala, 752 67, Sweden

    Joakim Nivre

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Merlo, P., Bunt, H., Nivre, J. (2010). Current Trends in Parsing Technology. In: Bunt, H., Merlo, P., Nivre, J. (eds) Trends in Parsing Technology. Text, Speech and Language Technology, vol 43. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9352-3_1

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