Difference between revisions of "Exploiting Structure and Content of Wikipedia for Query Expansion in the Context of Question Answering"
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+ | | title = Exploiting Structure and Content of Wikipedia for Query Expansion in the Context of Question Answering | ||
+ | | date = 2009 | ||
+ | | authors = [[Surya Ganesh]]<br />[[Vasudeva Varma]] | ||
+ | | link = http://www.aclweb.org/anthology/R/R09/R09-1020.pdf | ||
+ | }} | ||
'''Exploiting Structure and Content of Wikipedia for Query Expansion in the Context of Question Answering''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Surya Ganesh]] and [[Vasudeva Varma]]. | '''Exploiting Structure and Content of Wikipedia for Query Expansion in the Context of Question Answering''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Surya Ganesh]] and [[Vasudeva Varma]]. | ||
== Overview == | == Overview == | ||
Retrieving answer containing passages is a challenging task in Question Answering. In this paper authors describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and [[structured information]] (link structure and category information) of [[Wikipedia]] to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. Authors conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using query expansion method. | Retrieving answer containing passages is a challenging task in Question Answering. In this paper authors describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and [[structured information]] (link structure and category information) of [[Wikipedia]] to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. Authors conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using query expansion method. |
Revision as of 00:02, 5 February 2021
Authors | Surya Ganesh Vasudeva Varma |
---|---|
Publication date | 2009 |
Links | Original |
Exploiting Structure and Content of Wikipedia for Query Expansion in the Context of Question Answering - scientific work related to Wikipedia quality published in 2009, written by Surya Ganesh and Vasudeva Varma.
Overview
Retrieving answer containing passages is a challenging task in Question Answering. In this paper authors describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and structured information (link structure and category information) of Wikipedia to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. Authors conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using query expansion method.