Handbook of formal languages, vol. 3
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
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
An empirical evaluation of Probabilistic Lexicalized Tree Insertion Grammars
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '95 Proceedings of the 33rd 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
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Two statistical parsing models applied to the Chinese Treebank
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Automated extraction of Tree-Adjoining Grammars from treebanks
Natural Language Engineering
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fast, accurate deterministic parser for Chinese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Morphology and reranking for the statistical parsing of Spanish
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised methods for head assignments
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Better informed training of latent syntactic features
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Inducing compact but accurate tree-substitution grammars
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Parsing German with latent variable grammars
PaGe '08 Proceedings of the Workshop on Parsing German
Head-driven PCFGs with latent-head statistics
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
The hidden TAG model: synchronous grammars for parsing resource-poor languages
TAGRF '06 Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Exploiting heterogeneous treebanks for parsing
ACL '09 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: Volume 1 - Volume 1
Cross parser evaluation and tagset variation: a French treebank study
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Transition-based parsing of the Chinese treebank using a global discriminative model
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Building a large syntactically-annotated corpus of Vietnamese
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
Refining grammars for parsing with hierarchical semantic knowledge
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Syntactic parsing with hierarchical modeling
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Head finders inspection: an unsupervised optimization approach
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Phrase structure parsing with dependency structure
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Syntactic processing using the generalized perceptron and beam search
Computational Linguistics
Parsing noun phrases in the penn treebank
Computational Linguistics
Inducing head-driven PCFGs with latent heads: refining a tree-bank grammar for parsing
ECML'05 Proceedings of the 16th European conference on Machine Learning
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
The challenges of parsing Chinese with combinatory categorial grammar
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Many recent statistical parsers rely on a preprocessing step which uses hand-written, corpus-specific rules to augment the training data with extra information. For example, head-finding rules are used to augment node labels with lexical heads. In this paper, we provide machinery to reduce the amount of human effort needed to adapt existing models to new corpora: first, we propose a flexible notation for specifying these rules that would allow them to be shared by different models; second, we report on an experiment to see whether we can use Expectation-Maximization to automatically fine-tune a set of hand-written rules to a particular corpus.