@inProceedings{
 title = {Maximum Entropy Markov Models for Information Extraction and Segmentation},
 type = {inProceedings},
 year = {2000},
 pages = {591-598},
 volume = {3},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.43.351&rep=rep1&type=pdf},
 publisher = {Morgan Kaufmann, San Francisco, CA},
 editors = {[object Object]},
 id = {016f3f0c-85fd-35eb-8330-f698c8e8daea},
 created = {2010-11-13T05:17:45.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2019-09-17T09:34:57.277Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {McCallum2000a},
 notes = {Falta lerCitado 299 vezes<br/>http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=8C93442C94F6F7135C6F71670CB3C0F4?doi=10.1.1.116.2034},
 private_publication = {false},
 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 mod- eled 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 ob- servations to be represented as arbitrary overlap- ping features (such as word, capitalization, for- matting, part-of-speech), and de nes the condi- tional probability of state sequences given ob- servation sequences. It does this by using the maximum entropy framework to t a set of expo- nential 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.},
 bibtype = {inProceedings},
 author = {McCallum, Andrew and Freitag, Dayne and Pereira, Fernando},
 booktitle = {Proc 17th International Conf on Machine Learning}
}

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