Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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Information Retrieval
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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Natural Language Engineering
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Subtree mining for question classification problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Question interpretation in QA@L²F
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Proceedings of the Seventeenth Australasian Document Computing Symposium
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Knowledge-Based Systems
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Classifying what-type questions into proper semantic categories is found more challenging than classifying other types in question answering systems. In this paper, we propose to classify what-type questions by head noun tagging. The approach highlights the role of head nouns as the category discriminator of what-type questions. To reduce the semantic ambiguities of head noun, we integrate local syntactic feature, semantic feature and category dependency among adjacent nouns with Conditional Random Fields (CRFs). Experiments on standard question classification data set show that the approach achieves state-of-the-art performances.