Syntax-motivated context windows of morpho-lexical features for recognizing time and event expressions in natural language

Authors:
Hector Llorens;Estela Saquete;Borja Navarro
Affiliations:
University of Alicante, Alicante, Spain;University of Alicante, Alicante, Spain;University of Alicante, Alicante, Spain
Venue:
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Year:
2011

Citing 4
Cited 0

SemEval-2010 task 13: TempEval-2

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
KUL: Recognition and normalization of temporal expressions

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation

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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.