Enhanced answer type inference from questions using sequential models

Krishnan, Vijay ; Das, Sujatha ; Chakrabarti, Soumen (2005) Enhanced answer type inference from questions using sequential models In: HLT '05 Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, October 06 - 08, 2005, Vancouver, British Columbia, Canada.

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Official URL: http://dl.acm.org/citation.cfm?id=1220615&CFID=862...


Question classification is an important step in factual Question Answering (QA) and other dialog systems. Several attempts have been made to apply statistical machine learning approaches, including Support Vector Machines (SVMs) with sophisticated features and kernels. Curiously, the payoff beyond a simple bag-of words representation has been small. We show that most questions reveal their class through a short contiguous token subsequence, which we call its informer span. Perfect knowledge of informer spans can enhance accuracy from 79.4% to 88% using linear SVMs on standard benchmarks. In contrast, standard heuristics based on shallow pattern-matching give only a 3% improvement, showing that the notion of an informer is non-trivial. Using a novel multi-resolution encoding of the question’s parse tree, we induce a Conditional Random Field (CRF) to identify informer spans with about 85% accuracy. Then we build a meta-classifier using a linear SVM on the CRF output, enhancing accuracy to 86.2%, which is better than all published numbers.

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to HLT '05 Proceedings of the Conference, Association for Computational Linguistics.
ID Code:100083
Deposited On:12 Feb 2018 12:28
Last Modified:12 Feb 2018 12:28

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