Fast Boosting-based Part-of-Speech Tagging and Text Chunking with Efficient Rule Representation for Sequential Labeling

This paper proposes two techniques for fast sequential labeling such as part-of-speech (POS) tagging and text chunking. The first technique is a boosting-based algorithm that learns rules represented by combination of features. To avoid time-consuming evaluation of combination, we divide features into not used ones and used ones for learning combination. The other is a rule representation. Usual POS taggers and text chunkers decide the tag of each word by using the features generated from the word and its surrounding words. Thus similar rules, for example, that consist of the same set of words but only differ in locations from current words, are generated. We use a rule representation that enables us to merge such rules. We evaluate our methods with POS tagging and text chunking. The experimental results show that our methods show faster processing speed than taggers and chunkers without our methods while maintaining accuracy.