Paper: A Practically Unsupervised Learning Method To Identify Single-Snippet Answers To Definition Questions On The Web

ACL ID H05-1041
Title A Practically Unsupervised Learning Method To Identify Single-Snippet Answers To Definition Questions On The Web
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

We present a practically unsupervised learning method to produce single-snippet answers to definition questions in ques- tion answering systems that supplement Web search engines. The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples, which are then used to train an SVM to separate the two classes. We show experimentally that the proposed method is viable, that it outperforms the alterna- tive of training the system on questions and news articles from TREC, and that it helps the search engine handle definition questions significantly better.