Maximum-entropy Markov model
In machine learning, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learned are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging[1] and information extraction.[2]
MEMMs potentially suffer from the "label bias problem," where states with low-entropy transition distributions "effectively ignore their observations." Conditional random fields were designed to overcome this weakness.[3]
References
- ^ Toutanova, Kristina; Manning, Christopher D. (2000). "Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger". Proc. J. SIGDAT Conf. on Empirical Methods in NLP and Very Large Corpora (EMNLP/VLC-2000). pp. 63–70.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ McCallum, Andrew; Freitag, Dayne; Pereira, Fernando (2000). "Maximum Entropy Markov Models for Information Extraction and Segmentation". Proc. ICML 2000. pp. 591–598.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ Lafferty, John; McCallum, Andrew; Pereira, Fernando (2001). "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data". Proc. ICML 2001.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help)
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