Difference between revisions of "Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia"

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{{Infobox work
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| title = Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia
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| date = 2012
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| authors = [[M. Atif Qureshi]]<br />[[Colm O'Riordan]]<br />[[Gabriella Pasi]]
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| doi = 10.1145/2396761.2398680
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| link = http://dl.acm.org/ft_gateway.cfm?id=2398680&amp;type=pdf
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}}
 
'''Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[M. Atif Qureshi]], [[Colm O'Riordan]] and [[Gabriella Pasi]].
 
'''Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[M. Atif Qureshi]], [[Colm O'Riordan]] and [[Gabriella Pasi]].
  
 
== Overview ==
 
== Overview ==
 
Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of [[Wikipedia]] articles and the Wikipedia category graph. Authors performed some experiments over the document collection of Web sites of different post-graduate schools. Authors preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.
 
Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of [[Wikipedia]] articles and the Wikipedia category graph. Authors performed some experiments over the document collection of Web sites of different post-graduate schools. Authors preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.

Revision as of 11:59, 24 March 2021


Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia
Authors
M. Atif Qureshi
Colm O'Riordan
Gabriella Pasi
Publication date
2012
DOI
10.1145/2396761.2398680
Links
Original

Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia - scientific work related to Wikipedia quality published in 2012, written by M. Atif Qureshi, Colm O'Riordan and Gabriella Pasi.

Overview

Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. Authors performed some experiments over the document collection of Web sites of different post-graduate schools. Authors preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.