Syntax-Motivated Context Windows of Morpho-Lexical Features for Recognizing Time and Event Expressions in Natural Language

Abstract

We present an analysis of morpho-lexical features to learn SVM models for recognizing TimeML time and event expressions. We evaluate over the TempEval-2 data, the features: word, lemma, and PoS in isolation, in different size static-context windows, and in a syntax-motivated dynamic-context windows defined in this paper. The results show that word, lemma, and PoS introduce complementary advantages and their combination achieves the best performance; this performance is improved using context, and, with dynamic-context, timex recognition is improved to reach state-of-art performance. Although more complex approaches improve the efficacy, the morpho-lexical features can be obtained more efficiently and show a reasonable efficacy.