• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Maximum entropy markov models for information extraction and segmentation (2000)

Cached

  • Download as a PDF

Download Links

  • [www.seas.upenn.edu]
  • [www.seas.upenn.edu]
  • [www.cs.iastate.edu]
  • [www.cs.iastate.edu]
  • [web.cs.iastate.edu]
  • [www.cs.cmu.edu]
  • [www.ai.mit.edu]
  • [www.cs.cmu.edu]
  • [www.cs.umass.edu]
  • [www.cs.cmu.edu]
  • [people.csail.mit.edu]
  • [people.csail.mit.edu]
  • [www.ai.mit.edu]
  • [courses.csail.mit.edu]
  • [www.cs.utah.edu]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Andrew McCallum , Dayne Freitag , Fernando Pereira
Citations:561 - 18 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@INPROCEEDINGS{McCallum00maximumentropy,
    author = {Andrew McCallum and Dayne Freitag and Fernando Pereira},
    title = {Maximum entropy markov models for information extraction and segmentation},
    booktitle = {},
    year = {2000},
    pages = {591--598},
    publisher = {Morgan Kaufmann}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We present positive experimental results on the segmentation of FAQ’s.

Keyphrases

information extraction    maximum entropy markov model    observation sequence    discrete vocabulary    new markovian sequence model    powerful probabilistic tool    part-of-speech tagging    text segmentation    state sequence    previous state    maximum entropy framework    hidden markov model    exponential model    present positive experimental result    sequential data    hmm parameter    multinomial distribution    arbitrary overlapping feature    conditional probability    many text-related task   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University