Mining class-correlated patterns for sequence labeling

Abstract

Sequence labeling is the task of assigning a label sequence to an observation sequence. Since many methods to solve this problem depend on the specification of predictive features, automated methods for their derivation are desirable. Unlike in other areas of pattern-based classification, however, no algorithm to directly mine class-correlated patterns for sequence labeling has been proposed so far. We introduce the novel task of mining class-correlated sequence patterns for sequence labeling and present a supervised pattern growth algorithm to find all patterns in a set of observation sequences, which correlate with the assignment of a fixed sequence label no less than a user-specified minimum correlation constraint. From the resulting set of patterns, features for a variety of classifiers can be obtained in a straightforward manner. The efficiency of the approach and the influence of important parameters are shown in experiments on several biological datasets. © 2010 Springer-Verlag.