Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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We study the problem of identifying phrase structure. We formalize it as the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, and develop two general approaches for it. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observations structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We also develop efficient algorithms under both models and study them experimentally in the context of shallow parsing.