Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Supertagging: an approach to almost parsing
Computational Linguistics
Part-of-speech induction from scratch
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Recovering latent information in treebanks
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Intricacies of Collins' Parsing Model
Computational Linguistics
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Unsupervised induction of labeled parse trees by clustering with syntactic features
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Cross parser evaluation and tagset variation: a French treebank study
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
A generative re-ranking model for dependency parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Factors affecting the accuracy of Korean parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Head finders inspection: an unsupervised optimization approach
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Accurate parsing with compact tree-substitution grammars: Double-DOP
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We present several algorithms for assigning heads in phrase structure trees, based on different linguistic intuitions on the role of heads in natural language syntax. Starting point of our approach is the observation that a head-annotated treebank defines a unique lexicalized tree substitution grammar. This allows us to go back and forth between the two representations, and define objective functions for the unsupervised learning of head assignments in terms of features of the implicit lexicalized tree grammars. We evaluate algorithms based on the match with gold standard head-annotations, and the comparative parsing accuracy of the lexicalized grammars they give rise to. On the first task, we approach the accuracy of hand-designed heuristics for English and inter-annotation-standard agreement for German. On the second task, the implied lexicalized grammars score 4% points higher on parsing accuracy than lexicalized grammars derived by commonly used heuristics.