ABSTRACT
We present a novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner. Traditional deterministic parsing algorithms are based on a shift-reduce framework: they traverse the sentence from left-to-right and, at each step, perform one of a possible set of actions, until a complete tree is built. A drawback of this approach is that it is extremely local: while decisions can be based on complex structures on the left, they can look only at a few words to the right. In contrast, our algorithm builds a dependency tree by iteratively selecting the best pair of neighbours to connect at each parsing step. This allows incorporation of features from already built structures both to the left and to the right of the attachment point. The parser learns both the attachment preferences and the order in which they should be performed. The result is a deterministic, best-first, O(nlogn) parser, which is significantly more accurate than best-first transition based parsers, and nears the performance of globally optimized parsing models.
- Sabine Buchholz and Erwin Marsi. 2006. CoNLL-X shared task on multilingual dependency parsing. In Proc. of CoNLL. Google ScholarDigital Library
- Xavier Carreras. 2007. Experiments with a higher-order projective dependency parser. In Proc. of CoNLL Shared Task, EMNLP-CoNLL.Google Scholar
- Michael Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proc of EMNLP. Google ScholarDigital Library
- Markus Dreyer, David A. Smith, and Noah A. Smith. 2006. Vine parsing and minimum risk reranking for speed and precision. In Proc. of CoNLL, pages 201--205, Morristown, NJ, USA. Association for Computational Linguistics. Google ScholarDigital Library
- Jason Eisner and Noah A. Smith. 2005. arsing with soft and hard constraints on dependency length. In Proc. of IWPT. Google ScholarDigital Library
- Liang Huang, Wenbin Jiang, and Qun Liu. 2009. Bilingually-constrained (monolingual) shift-reduce parsing. In Proc of EMNLP. Google ScholarDigital Library
- Richard Johansson and Pierre Nugues. 2007. Extended constituent-to-dependency conversion for english. In Proc of NODALIDA.Google Scholar
- Ryan McDonald and Joakim Nivre. 2007. Characterizing the errors of data-driven dependency parsing models. In Proc. of EMNLP.Google Scholar
- Ryan McDonald and Fernando Pereira. 2006. Online learning of approximate dependency parsing algorithms. In Proc of EACL.Google Scholar
- Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proc of ACL. Google ScholarDigital Library
- Tetsuji Nakagawa. 2007. Multilingual dependency parsing using global features. In Proc. of EMNLP-CoNLL.Google Scholar
- Joakim Nivre and Ryan McDonald. 2008. Integrating graph-based and transition-based dependency parsers. In Proc. of ACL, pages 950--958, Columbus, Ohio, June. Association for Computational Linguistics.Google Scholar
- Joakim Nivre, Johan Hall, and Jens Nillson. 2006. Malt-Parser: A data-driven parser-generator for dependency parsing. In Proc. of LREC.Google Scholar
- Joakim Nivre, Johan Hall, Sandra Kübler, Ryan Mcdonald, Jens Nilsson, Sebastian Riedel, and Deniz Yuret. 2007. The CoNLL 2007 shared task on dependency parsing. In Proc. of EMNLP-CoNLL.Google Scholar
- Joakim Nivre. 2004. Incrementality in deterministic dependency parsing. In Incremental Parsing: Bringing Engineering and Cognition Together, ACL-Workshop. Google ScholarDigital Library
- Sebastian Riedel and James Clarke. 2006. Incremental integer linear programming for non-projective dependency parsing. In Proc. of EMNLP 2006, July. Google ScholarDigital Library
- Kenji Sagae and Alon Lavie. 2006a. A best-first probabilistic shift-reduce parser. In Proc of ACL. Google ScholarDigital Library
- Kenji Sagae and Alon Lavie. 2006b. Parser combination by reparsing. In Proc of NAACL. Google ScholarDigital Library
- Federico Sangati and Chiara Mazza. 2009. An english dependency treebank à la tesnière. In Proc of TLT8.Google Scholar
- Libin Shen and Aravind K. Joshi. 2008. Ltag dependency parsing with bidirectional incremental construction. In Proc of EMNLP. Google ScholarDigital Library
- Libin Shen, Giorgio Satta, and Aravind K. Joshi. 2007. Guided learning for bidirectional sequence classification. In Proc of ACL.Google Scholar
- Ivan Titov and James Henderson. 2007. Fast and robust multilingual dependency parsing with a generative latent variable model. In Proc. of EMNLP-CoNLL.Google Scholar
- Yamada and Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proc. of IWPT.Google Scholar
- Yue Zhang and Stephen Clark. 2008. A tale of two parsers: investigating and combining graph-based and transition-based dependency parsing using beam-search. In Proc of EMNLP. Google ScholarDigital Library
Recommendations
Effective Representation for Easy-First Dependency Parsing
PRICAI 2019: Trends in Artificial IntelligenceAbstractEasy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing is represented by various subtrees, whose internal structural information is the key lead for later parsing ...
Improve Chinese Semantic Dependency Parsing via Syntactic Dependency Parsing
IALP '12: Proceedings of the 2012 International Conference on Asian Language ProcessingWe address the problem of Chinese semantic dependency parsing. Dependency parsing is traditionally oriented to syntax analysis, which we denote by syntactic dependency parsing to distinguish it from semantic dependency parsing. In this paper, firstly we ...
Comments