Paper: Enhanced Answer Type Inference From Questions Using Sequential Models

ACL ID H05-1040
Title Enhanced Answer Type Inference From Questions Using Sequential Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

Question classification is an important step in factual question answering (QA) and other dialog systems. Several at- tempts have been made to apply statistical machine learning approaches, including Support Vector Machines (SVMs) with sophisticated features and kernels. Curi- ously, 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 subse- quence, which we call its informer span. Perfect knowledge of informer spans can enhance accuracy from 79.4% to 88% using linear SVMs on standard bench- marks. In contrast, standard heuristics based on shallow pattern-matching give only a 3% improvement, showing that the notion of an informer is non-trivial. Us- ing a novel multi-resolution encoding of t...