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Verbocean:Mining the web for fine-grained semantic verb relations Position1 #258537
Demo of VerbOcean at http://demo.patrickpantel.com/demos/verbocean/ Full list of 3,477 unique verbs in VerbOcean at http://www.patrickpantel.com/download/data/verbocean/verbocean-verbs.2004-05-20.txt Unrefined VerbOcean, 22,306 relations (line-based gzipped text file, self-explanatory format) at http://www.patrickpantel.com/cgi-bin/web/tools/getfile.pl?type=data&id=verbocean/verbocean.unrefined.2004-05-20.txt.gz

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OpenSherlock Project »OpenSherlock ProjectOpenSherlock Project☜Fabricating possibly many open source variants along the lines of IBMs Watson. The projects name has changed from SolrDrWatson to SolrSherlock, with thanks to Tom Munnecke. Migrating to OpenSherlock concept where we generalize beyond Solr as the platform core.☜F1CEB7

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Conference Papers »Conference PapersConference Papers☜☜FFB597

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Verbocean:Mining the web for fine-grained semantic verb relationsVerbocean:Mining the web for fine-grained semantic verb relations☜☜59C6EF

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Link[1] Verbocean:Mining the web for fine-grained semantic verb relations

Author: Timothy Chklovski, Patrick Pantel
Publication info: 2004
Cited by: Jack Park 8:47 PM 6 March 2013 GMT
URL:

http://malt.ml.cmu.edu/mw/index.php/Chklovski_and_Pantel_(2004)_Verbocean:Mining_the_web_for_fine-grained_semantic_verb_relations

Excerpt / Summary

Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy.

Link[2] A Critique of “VerbOcean: Mining the Web for Fine- Grained Semantic Verb Relations

Author: Kino Coursey
Cited by: Jack Park 8:52 PM 6 March 2013 GMT
URL:

http://www.daxtron.com/csce6330/Critique%20of%20VerbOcean.pdf

Excerpt / Summary

The key to the entire process is finding good patterns. Given that for both symmetric and asymmetric relationship you want the pattern that maximizes the Sp(V1,V2) formula, one can hunt for patterns using Google wildcards. For instance “bought * * sold” and find out what terms fill the wildcards. One can then rank the patterns by the Sp(V1,V2) formula. The primary difference would be using Google directly instead of DIRT.

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Entered by:- Jack Park
NodeID: #258537
Node type: Position
Entry date (GMT): 3/6/2013 8:46:00 PM
Last edit date (GMT): 3/6/2013 9:00:00 PM
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