Corpus ID: 14509422

The Use of Classifiers in Sequential Inference

@article{Punyakanok2000TheUO,
  title={The Use of Classifiers in Sequential Inference},
  author={Vasin Punyakanok and D. Roth},
  journal={ArXiv},
  year={2000},
  volume={cs.LG/0111003}
}
  • Vasin Punyakanok, D. Roth
  • Published 2000
  • Computer Science, Mathematics
  • ArXiv
  • We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem - identifying phrase structure. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observation structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 38 REFERENCES
    A Learning Approach to Shallow Parsing
    • 103
    • PDF
    Maximum Entropy Markov Models for Information Extraction and Segmentation
    • 1,468
    • PDF
    Learning to Resolve Natural Language Ambiguities: A Unified Approach
    • 220
    • PDF
    A stochastic parts program and noun phrase parser for unrestricted text
    • 611
    • PDF
    FASTUS: A Finite-state Processor for Information Extraction from Real-world Text
    • 335
    • PDF
    Parsing By Chunks
    • 918
    • PDF
    Text Chunking using Transformation-Based Learning
    • 1,301
    • PDF
    A Memory-Based Approach to Learning Shallow Natural Language Patterns
    • 116
    • PDF
    Co-Occurrence and Transformation in Linguistic Structure
    • 253