Difference between revisions of "Enhancing cluster labeling using Wikipedia"

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{{Publication
 
{{Publication
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|type=Conference paper
 
|title=Enhancing cluster labeling using Wikipedia
 
|title=Enhancing cluster labeling using Wikipedia
|authors=David Carmel, Haggai Roitman, Naama Zwerdling  
+
|authors=David Carmel, Haggai Roitman, Naama Zwerdling
 
|published_in=SIGIR '09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
 
|published_in=SIGIR '09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
|type=Conference paper
 
|peer_reviewed=yes
 
|language=English
 
|month=July
 
 
|year=2009
 
|year=2009
 +
|month=July
 
|dates=19-23
 
|dates=19-23
 +
|pages=139-146
 
|conference_location=Boston, MA, United states
 
|conference_location=Boston, MA, United states
 
|publisher=Association for Computing Machinery
 
|publisher=Association for Computing Machinery
|pages=139-146
+
|url=http://dl.acm.org/citation.cfm?id=1571967
|abstract=This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text pure statistical methods have difficulty in identifying them as good descriptors. Furthermore our experiments show that for more than 85\% of the clusters in our test collection the manual label (or an inflection or a synonym of it) appears in the top five labels recommended by our system. Copyright 2009 {ACM.}"
+
|peer_reviewed=Yes
 +
|added_by_wikilit_team=Added on initial load
 +
|article_language=English
 +
|abstract=This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text pure statistical methods have difficulty in identifying them as good descriptors. Furthermore our experiments show that for more than 85% of the clusters in our test collection the manual label (or an inflection or a synonym of it) appears in the top five labels recommended by our system.  
 +
|doi=10.1145/1571941.1571967
 +
|gscites=14099639641125736698
 +
|topics=Ranking and clustering systems
 +
|domains=Computer science
 
|research_questions=This work investigates cluster labeling enhancement by uti-
 
|research_questions=This work investigates cluster labeling enhancement by uti-
 
lizing Wikipedia, the free on-line encyclopedia. We describe
 
lizing Wikipedia, the free on-line encyclopedia. We describe
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dent judges and the top evaluated candidates are recom-
 
dent judges and the top evaluated candidates are recom-
 
mended for labeling.
 
mended for labeling.
|topics=Ranking and clustering systems
 
|domains=Computer science
 
 
|theory_type=Design and action
 
|theory_type=Design and action
 
|wikipedia_coverage=Sample data
 
|wikipedia_coverage=Sample data
 
|theories=Undetermined
 
|theories=Undetermined
 
|research_design=Experiment
 
|research_design=Experiment
|collected_datatype=Archival records, Wikipedia pages
+
|data_source=Archival records, Experiment responses, Wikipedia pages
 
|collected_data_time_dimension=Cross-sectional
 
|collected_data_time_dimension=Cross-sectional
 
|unit_of_analysis=Article
 
|unit_of_analysis=Article
|wikipedia_data_extraction=Clone
+
|wikipedia_data_extraction=Dump
 
|wikipedia_page_type=Article
 
|wikipedia_page_type=Article
 
|wikipedia_language=Not specified
 
|wikipedia_language=Not specified

Latest revision as of 20:25, January 30, 2014

Publication (help)
Enhancing cluster labeling using Wikipedia
Authors: David Carmel, Haggai Roitman, Naama Zwerdling [edit item]
Citation: SIGIR '09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval  : 139-146. 2009 July 19-23. Boston, MA, United states. Association for Computing Machinery.
Publication type: Conference paper
Peer-reviewed: Yes
Database(s):
DOI: 10.1145/1571941.1571967.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Enhancing cluster labeling using Wikipedia is a publication by David Carmel, Haggai Roitman, Naama Zwerdling.


[edit] Abstract

This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text pure statistical methods have difficulty in identifying them as good descriptors. Furthermore our experiments show that for more than 85% of the clusters in our test collection the manual label (or an inflection or a synonym of it) appears in the top five labels recommended by our system.

[edit] Research questions

"This work investigates cluster labeling enhancement by uti- lizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candi- date labels from Wikipedia in addition to important terms that are extracted directly from the text. The“labeling qual- ity” of each candidate is then evaluated by several indepen- dent judges and the top evaluated candidates are recom- mended for labeling."

Research details

Topics: Ranking and clustering systems [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Archival records, Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: Not specified [edit item]

[edit] Conclusion

"Cluster labeling withWikipedia is extremely successful, as shown by our results, especially in collections of documents whose topics are covered well by Wikipedia concepts. For domain specific collections, with topics that are not com- pletely covered by Wikipedia, the proposed candidates may hurt the system’s performance due to their irrelevance to the documents’ topics. For such collections, an intelligent decision should be made regarding the use of Wikipedia or another external resource; alternatively, a choice could be made to focus only on inner terms for labeling. The deci- sion should be made by analyzing the given collection with respect to Wikipedia. Developing such a collection specific decision making as part of the labeling framework is left for further research."

[edit] Comments

""Cluster labeling withWikipedia is extremely successful, as shown by our results, especially in collections of documents whose topics are covered well by Wikipedia concepts." p. 146"


Further notes[edit]