Class-based n-gram models of natural language
Computational Linguistics
Grammatical Trigrams: A Probabilistic Model of Link Grammar
Grammatical Trigrams: A Probabilistic Model of Link Grammar
Bayesian grammar induction for language modeling
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A transformational-based learner for dependency grammars in discharge summaries
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
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A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.