Overview:
As the amount of information grows on the web, it becomes harder to find information. Research on Question Answering Systems aims at making the task of finding information easier. The goal is to replace current search technologies, which are based solely on key-word search, with the ability to process questions and find explicit answers for them.
Relevant Software:
Relevant Publications:
- X. Li and D. Roth, Learning Question Classifiers: The Role of Semantic Information. Journal of Natural Language Engineering (2005)
- V. Punyakanok, D. Roth, W. Yih, and D. Zimak, Natural Language Inference via Dependency Tree Mapping: An Application to Question Answering. Computational Linguistics (2004)
- X. Li, D. Roth, and K. Small, The Role of Semantic Information in Learning Question Classifiers. Proc. of the International Joint Conference on Natural Language Processing (IJCNLP) (2004)
- V. Punyakanok, D. Roth, and W. Yih, Mapping Dependencies Trees: An Application to Question Answering. Proceedings of AI & Math (2004)
- X. Li and D. Roth, Learning Question Classifiers. Proc. the International Conference on Computational Linguistics (COLING) (2002) pp. 556--562
- D. Roth, C. Cumby, X. Li, P. Morie, R. Nagarajan, V. Punyakanok, N. Rizzolo, K. Small, and W. Yih, Question-Answering via Enhanced Understanding of Questions. TREC (2002)
- D. Roth, G. Kao, X. Li, R. Nagarajan, V. Punyakanok, N. Rizzolo, W. Yih, C. Alm, and L. Moran, Learning Components for a Question Answering System. TREC (2001) pp. 539-548
