Semi-Markov Conditional Random Fields for Information Extraction

Abstract

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. 1