Difference between revisions of "Apappu writeup of Freitag et al."
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Latest revision as of 11:42, 3 September 2010
This is a review of the paper Frietag_2000_Maximum_Entropy_Markov_Models_for_Information_Extraction_and_Segmentation by user:apappu.
- This paper talks about combining the advantages of HMMs and Maximum Entropy models.
- Authors state two problems with traditional approach (of HMM):
* not addressing the interdependency between features * emphasis on maximizing the likelihood of the observation sequence when task is conditional in nature (state sequence given observation sequence).
- In one of the variant approaches suggested by authors: observations in states instead of transitions approach has an interesting advantage of reducing the number of parameters and project it useful for applications when training data is sparse.
- I also liked how it was shown that structure regularities actually matters besides line features. (refers to Table 4)