Clustering by Committee - Revision history

Pdturney: Reverted edits by Edward518 (Talk); changed back to last version by Pdturney

2008-01-08T13:44:44Z

Reverted edits by Edward518 (Talk); changed back to last version by Pdturney

← Older revision		Revision as of 13:44, 8 January 2008
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	[[Category:Textual Entailment Portal]]		[[Category:Textual Entailment Portal]]
	[[Category:Word sense disambiguation]]		[[Category:Word sense disambiguation]]
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Edward518 at 10:21, 8 January 2008

2008-01-08T10:21:53Z

← Older revision		Revision as of 10:21, 8 January 2008
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	[[Category:Textual Entailment Portal]]		[[Category:Textual Entailment Portal]]
	[[Category:Word sense disambiguation]]		[[Category:Word sense disambiguation]]
			北京万达火车票预定中心

			[http://www.huochepiao168.cn 火车票] [http://www.huochepiao168.cn 订火车票] [http://www.huochepiao168.cn 北京火车票] [http://www.huochepiao168.cn 火车票预定]
			[http://www.huochepiao168.cn 火车票预订] [http://www.huochepiao168.cn 火车票查询]
			[http://www.huochepiao168.cn 北京火车票预定] [http://www.huochepiao168.cn 北京火车票查询]
			[http://www.huochepiao168.cn 北京火车票预订]
			[http://www.chepiao168.cn 火车票] [http://www.chepiao168.cn 订火车票] [http://www.chepiao168.cn 北京火车票] [http://www.chepiao168.cn 火车票预定]
			[http://www.chepiao168.cn 火车票预订] [http://www.chepiao168.cn 火车票查询]
			[http://www.chepiao168.cn 北京火车票预定] [http://www.chepiao168.cn 北京火车票查询]
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Pdturney at 01:12, 7 February 2007

2007-02-07T01:12:10Z

← Older revision		Revision as of 01:12, 7 February 2007
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	Using sets of representative elements, called committees, CBC discovers concept signatures that unambiguously describe the members of a possible concept (e.g. diseases, hockey goalies, and colors). Concept signatures are templates of grammatical relations that apply to most of the instances of the concept (lexical co-occurrence vectors). The algorithm initially discovers committees that are well scattered in the similarity space. It then proceeds by assigning words to their most similar committees, each of which represents a final cluster. After assigning a word to a committee, CBC removes their overlapping features (syntactical co-occurrences) from the word before assigning it to another committee. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses.		Using sets of representative elements, called committees, CBC discovers concept signatures that unambiguously describe the members of a possible concept (e.g. diseases, hockey goalies, and colors). Concept signatures are templates of grammatical relations that apply to most of the instances of the concept (lexical co-occurrence vectors). The algorithm initially discovers committees that are well scattered in the similarity space. It then proceeds by assigning words to their most similar committees, each of which represents a final cluster. After assigning a word to a committee, CBC removes their overlapping features (syntactical co-occurrences) from the word before assigning it to another committee. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses.

	On the task of recovering the concepts and word senses in WordNet, CBC achieved 61% precision and 51% recall. CBC outputs a flat list of concepts (i.e., there is no hierarchical information).		On the task of recovering the concepts and word senses in [[WordNet]], CBC achieved 61% precision and 51% recall. CBC outputs a flat list of concepts (i.e., there is no hierarchical information).

	== Acquiring the Resource ==		== Acquiring the Resource ==

Jonsafari: more specific cat; +cat

2006-11-17T18:05:48Z

more specific cat; +cat

← Older revision		Revision as of 18:05, 17 November 2006
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	[[Category:Knowledge Collections and Datasets]]		[[Category:Knowledge Collections and Datasets]]
	[[Category:~~Tools and~~ Software]]		[[Category:Software]]
	[[Category:Textual Entailment Portal]]		[[Category:Textual Entailment Portal]]
			[[Category:Word sense disambiguation]]

Timc at 23:42, 16 November 2006

2006-11-16T23:42:21Z

New page

'''CBC''' (Clustering by Committee) is both a clustering algorithm and a resulting knowledge collection created by [[Patrick Pantel]] and [[Dekang Lin]] at the [[University of Alberta]]. The algorithm is a general-purpose partitioning clustering algorithm. The authors have used it more specifically for automatically clustering documents and for automatically inducing concepts and word senses.

The CBC knowledge collection consists of concepts, which are clustered instances like the three shown below along with a template of typical grammatical contexts (lexical co-occurrence vectors) extracted from a textual corpora:

<code>
:(A) multiple sclerosis, diabetes, osteoporosis, cardiovascular disease, Parkinson's, rheumatoid arthritis, heart disease, asthma, cancer, hypertension, lupus, high blood pressure, arthritis, emphysema, epilepsy, cystic fibrosis, leukemia, hemophilia, Alzheimer, myeloma, glaucoma, schizophrenia, ...
:(B) Mike Richter, Tommy Salo, John Vanbiesbrouck, Curtis Joseph, Chris Osgood, Steve Shields, Tom Barrasso, Guy Hebert, Arturs Irbe, Byron Dafoe, Patrick Roy, Bill Ranford, Ed Belfour, Grant Fuhr, Dominik Hasek, Martin Brodeur, Mike Vernon, Ron Tugnutt, Sean Burke, Zach Thornton, Jocelyn Thibault, Kevin Hartman, Felix Potvin, ...
:(C) pink, red, turquoise, blue, purple, green, yellow, beige, orange, taupe, white, lavender, fuchsia, brown, gray, black, mauve, royal blue, violet, chartreuse, teal, gold, burgundy, lilac, crimson, garnet, coral, grey, silver, olive green, cobalt blue, scarlet, tan, amber, ...
</code>

Using sets of representative elements, called committees, CBC discovers concept signatures that unambiguously describe the members of a possible concept (e.g. diseases, hockey goalies, and colors). Concept signatures are templates of grammatical relations that apply to most of the instances of the concept (lexical co-occurrence vectors). The algorithm initially discovers committees that are well scattered in the similarity space. It then proceeds by assigning words to their most similar committees, each of which represents a final cluster. After assigning a word to a committee, CBC removes their overlapping features (syntactical co-occurrences) from the word before assigning it to another committee. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses.

On the task of recovering the concepts and word senses in WordNet, CBC achieved 61% precision and 51% recall. CBC outputs a flat list of concepts (i.e., there is no hierarchical information).

== Acquiring the Resource ==

Both an implementation of the CBC algorithm and the CBC knowledge collection is available for research purposes by contacting its authors.

== Demos ==

* [http://www.patrickpantel.com/Content/Demos/LexSem/cbc.htm CBC Search Engine]

== References ==

Please refer to either of the following publications when using this resource:
* Patrick Pantel. 2003. Clustering by Committee. Ph.D. Dissertation. Department of Computing Science, University of Alberta.
* Patrick Pantel and Dekang Lin. 2002. Discovering Word Senses from Text. In Proceedings of ACM Conference on Knowledge Discovery and Data Mining (KDD-02). pp. 613-619. Edmonton, Canada.

== Authors ==
[http://www.patrickpantel.com/ Patrick Pantel]

[http://www.cs.ualberta.ca/~lindek Dekang Lin]

[[Category:Knowledge Collections and Datasets]]
[[Category:Tools and Software]]
[[Category:Textual Entailment Portal]]