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dc:title "HTML Summary of #3122 \n\nCoherent Keyphrase Extraction via Web Mining\n\n";
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bibo:abstract "Keyphrases are useful for a variety of purposes,\nincluding summarizing, indexing, labeling,\ncategorizing, clustering, highlighting, browsing, and\nsearching. The task of automatic keyphrase extraction\nis to select keyphrases from within the text of a given\ndocument. Automatic keyphrase extraction makes it\nfeasible to generate keyphrases for the huge number of\ndocuments that do not have manually assigned\nkeyphrases. A limitation of previous keyphrase\nextraction algorithms is that the selected keyphrases are\noccasionally incoherent. That is, the majority of the\noutput keyphrases may fit together well, but there may\nbe a minority that appear to be outliers, with no clear\nsemantic relation to the majority or to each other. This\npaper presents enhancements to the Kea keyphrase\nextraction algorithm that are designed to increase the\ncoherence of the extracted keyphrases. The approach is\nto use the degree of statistical association among\ncandidate keyphrases as evidence that they may be\nsemantically related. The statistical association is\nmeasured using web mining. Experiments demonstrate\nthat the enhancements improve the quality of the\nextracted keyphrases. Furthermore, the enhancements\nare not domain-specific: the algorithm generalizes well\nwhen it is trained on one domain (computer science\ndocuments) and tested on another (physics documents)."^^xsd:string;
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dct:date "2003";
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skos:prefLabel "Machine Learning" .
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skos:prefLabel "Statistical Models" .
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foaf:givenName "Peter"^^xsd:string;
foaf:name "Peter Turney"^^xsd:string;
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