Handbook of formal languages, vol. 3
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
Using an annotated corpus as a stochastic grammar
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
What is the minimal set of fragments that achieves maximal parse accuracy?
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd 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
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
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Simple, accurate parsing with an all-fragments grammar
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Factors affecting the accuracy of Korean parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Judging grammaticality with count-induced tree substitution grammars
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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We provide a model that extends the splitmerge framework of Petrov et al. (2006) to jointly learn latent annotations and Tree Substitution Grammars (TSGs). We then conduct a variety of experiments with this model, first inducing grammars on a portion of the Penn Treebank and the Korean Treebank 2.0, and next experimenting with grammar refinement from a single nonterminal and from the Universal Part of Speech tagset. We present qualitative analysis showing promising signs across all experiments that our combined approach successfully provides for greater flexibility in grammar induction within the structured guidance provided by the treebank, leveraging the complementary natures of these two approaches.