Semi-Markov Conditional Random Fields for Information Extraction

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Sunita Sarawagi, William W. Cohen


We describe semi-Markov conditional random fields (semi-CRFs), a con- ditionally trained version of semi-Markov chains. Intuitively, a semi- CRF on an input sequence x outputs a “segmentation” of x, in which labels are assigned to segments (i.e., subsequences) of x rather than to individual elements xi of x. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. In spite of this additional power, exact learning and inference algorithms for semi-CRFs are polynomial-time—often only a small constant factor slower than conventional CRFs. In experiments on five named entity recognition problems, semi-CRFs generally outper- form conventional CRFs.