QuASM: a system for question answering using semi-structured data
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Enhanced answer type inference from questions using sequential models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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Question classification plays an important role in question answering systems. This paper presents the Conditional Random field (CRF) model based on Morpheme features for Tamil question classification. It is a process that analyzes a question and labels it based on its question type and expected answer type (EAT). The selected features are the morpheme parts of the question terms and its dependent terms. The main contribution in this work is in the way of selection of features for constructing CRF Model. They discriminates the position of expected answer type information with respect to question term's position. The CRF model to find out the phrase which contains the information about EAT is trained with tagged question corpus. The EAT is semantically derived by analyzing the phrase obtained from CRF engine using WordNet. The performance of this morpheme based CRF model is compared with the generic CRF engine.