Paper: A New Statistical Parser Based On Bigram Lexical Dependencies

ACL ID P96-1025
Title A New Statistical Parser Based On Bigram Lexical Dependencies
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1996
Authors

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability es- timation techniques are extended to calcu- late probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method per- forms at least as well as SPATTER (Mager- man 95; Jelinek et al. 94), which has the best published results for a statistical parser on this task. The simplicity of the approach means the model trains on 40,000 sentences in under 15 minutes. With a beam search strategy parsing speed can be improved to over 200 sentences a minute with negligible loss in accuracy.