Automatically Finding SignificantTopical Terms from Documents

摘要

With the pervasion of digital textual data, text mining is becoming more and more important to deriving competitivernadvantages. One factor for successful text mining applications is the ability of finding significant topical terms forrndiscovering interesting patterns or relationships. Document keyphrases are phrases carrying the most important topicalrnconcepts for a given document. In many applications, keyphrases as textual elements are better suited for text mining andrncould provide more discriminating power than single words. This paper describes an automatic keyphrase identificationrnprogram (KIP). KIP’s algorithm examines the composition of noun phrases and calculates their scores by looking up arndomain-specific glossary database; the ones with higher scores are extracted as keyphrases. KIP’s learning function canrnenrich its glossary database by automatically adding new identified keyphrases. KIP’s personalization feature allows the userrnbuild a glossary database specifically suitable for the area of his/her interest.