Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features

摘要

We developed a learning-based question classifier for question answering systems. A question classifier tries to predict the entity type of the possible answers to a given question written in natural language. We extracted several lexical, syntactic and semantic features and examined their usefulness for question classification. Furthermore we developed a weighting approach to combine features based on their importance. Our result on the well-known trec questions dataset is competitive with the state-of-the-art on this task.