Paper Info
Title
Automatic set expansion for list question answering
Abstract
This paper explores the use of set expansion (SE) to improve question answering (QA) when the expected answer is a list of entities belonging to a certain class. Given a small set of seeds, SE algorithms mine textual resources to produce an extended list including additional members of the class represented by the seeds. We explore the hypothesis that a noise-resistant SE algorithm can be used to extend candidate answers produced by a QA system and generate a new list of answers that is better than the original list produced by the QA system. We further introduce a hybrid approach which combines the original answers from the QA system with the output from the SE algorithm. Experimental results for several state-of-the-art QA systems show that the hybrid system performs better than the QA systems alone when tested on list question data from past TREC evaluations.
Year
Venue
Keywords
2008
EMNLP
automatic set expansion,hybrid system,list question data,original list,new list,qa system,list question answering,certain class,noise-resistant se algorithm,se algorithm,state-of-the-art qa system,extended list,system performance,question answering
Field
DocType
Volume
Question answering,Information retrieval,Computer science,Natural language processing,Set expansion,Artificial intelligence,Self-organizing list,Small set,Hybrid system
Conference
D08-1
Citations 
PageRank 
References 
15
0.95
6
Authors
4
Name
Order
Citations
PageRank
Richard C. Wang169839.93
Nico Schlaefer255926.50
William W. Cohen3101781243.74
Eric Nyberg41110101.91