Difference between revisions of "An Information Retrieval Expansion Model based on Wikipedia"
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| + | | title = An Information Retrieval Expansion Model based on Wikipedia | ||
| + | | date = 2014 | ||
| + | | authors = [[Li Xin Gan]]<br />[[Wei Tu]] | ||
| + | | doi = 10.4028/www.scientific.net/AMR.977.464 | ||
| + | | link = https://www.scientific.net/AMR.977.464 | ||
| + | }} | ||
'''An Information Retrieval Expansion Model based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Li Xin Gan]] and [[Wei Tu]]. | '''An Information Retrieval Expansion Model based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Li Xin Gan]] and [[Wei Tu]]. | ||
== Overview == | == Overview == | ||
Query expansion is one of the key technologies for improving precision and recall in [[information retrieval]]. In order to overcome limitations of single corpus, in this paper, semantic characteristics of [[Wikipedia]] corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in work is benefit to improve retrieval performance and to save search computational cost. | Query expansion is one of the key technologies for improving precision and recall in [[information retrieval]]. In order to overcome limitations of single corpus, in this paper, semantic characteristics of [[Wikipedia]] corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in work is benefit to improve retrieval performance and to save search computational cost. | ||
Revision as of 00:33, 3 June 2019
Authors | Li Xin Gan Wei Tu |
|---|---|
Publication date | 2014 |
DOI | 10.4028/www.scientific.net/AMR.977.464 |
Links | Original |
An Information Retrieval Expansion Model based on Wikipedia - scientific work related to Wikipedia quality published in 2014, written by Li Xin Gan and Wei Tu.
Overview
Query expansion is one of the key technologies for improving precision and recall in information retrieval. In order to overcome limitations of single corpus, in this paper, semantic characteristics of Wikipedia corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in work is benefit to improve retrieval performance and to save search computational cost.
