Information extraction with term frequencies

Authors:
T. R. Lynam;C. L. A. Clarke;G. V. Cormack
Affiliations:
University of Waterloo, Ontario, Canada;University of Waterloo, Ontario, Canada;University of Waterloo, Ontario, Canada
Venue:
HLT '01 Proceedings of the first international conference on Human language technology research
Year:
2001

Citing 2
Cited 2

Ranking suspected answers to natural language questions using predictive annotation

ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Examining the role of statistical and linguistic knowledge sources in a general-knowledge question-answering system

ANLC '00 Proceedings of the sixth conference on Applied natural language processing

Answer formulation for question-answering

AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Decision rule length as a basis for evaluation of attribute relevance

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Every day, millions of people use the internet to answer questions. Unfortunately, at present, there is no simple and successful means to consistently accomplish this goal. One common approach is to enter a few terms from a question into a Web search system and scan the resulting pages for the answer, a laborious process. To address this need, a question answering (QA) system was created to find and extract answers from a corpus. This system contains three parts: a parser for generating question queries and categories, a passage retrieval element, and an information extraction (IE) component. The extraction method was designed to elicit answers from passages collected by the information retrieval engine. The subject of this paper is the information extraction component. It is based on the premise that information related to the answer will be found many times in a large corpus like the Web.