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- W2005330168 abstract "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." @default.
- W2005330168 created "2016-06-24" @default.
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- W2005330168 date "2000-01-01" @default.
- W2005330168 modified "2023-09-23" @default.
- W2005330168 title "Shallow parsing by inferencing with classifiers" @default.
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- W2005330168 doi "https://doi.org/10.3115/1117601.1117625" @default.
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