Maximum Entropy Markov Models for Information Extraction and Segmentation

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.

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira
• Keywords: ICML 2000, Machine Learning, Markovian Sequence Model, Maximum Entropy Framework, Sequences Given Observation