The ATIS spoken language systems pilot corpus
HLT '90 Proceedings of the workshop on Speech and Natural Language
Statistical Language Learning
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Unsupervised language acquisition
Unsupervised language acquisition
Discovery of linguistic relations using lexical attraction
Discovery of linguistic relations using lexical attraction
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Pearl: a probabilistic chart parser
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Towards history-based grammars: using richer models for probabilistic parsing
HLT '91 Proceedings of the workshop on Speech and Natural Language
Decision tree parsing using a hidden derivation model
HLT '94 Proceedings of the workshop on Human Language Technology
The unsupervised learning of natural language structure
The unsupervised learning of natural language structure
The effect of alternative tree representations on tree bank grammars
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
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Grammar induction is one of the most important research areas of the natural language processing. The lack of a large Treebank, which is required in supervised grammar induction, in some natural languages such as Persian encouraged us to focus on unsupervised methods. We have found the Inside-Outside algorithm, introduced by Lari and Young, as a suitable platform to work on, and augmented IO with a history notion. The result is an improved unsupervised grammar induction method called History-based IO (HIO). Applying HIO to two very divergent natural languages (i.e., English and Persian) indicates that inducing more conditioned grammars improves the quality of the resultant grammar. Besides, our experiments on ATIS and WSJ show that HIO outperforms most current unsupervised grammar induction methods.