Automatically infer subject terms and documents associations through text mining

Automatically infer subject terms and documents associations through text mining

Subject indexing is an intellectual intensive process that bears many inherent uncertainties. Existing subject index systems generally produce binary outcomes on whether assigning an indexing term or not, which does not sufficiently reflect to which extent the indexing terms are associated with documents. On the other hand, probabilistic models have seen great success in capturing the uncertain...

Automatically Finding Significant Topical Terms from Documents

With the pervasion of digital textual data, text mining is becoming more and more important to deriving competitive advantages. One factor for successful text mining applications is the ability of finding significant topical terms for discovering interesting patterns or relationships. Document keyphrases are phrases carrying the most important topical concepts for a given document. In many appl...

Classifying Text Documents by Associating Terms With Text Categories

Evaluation of Automatically Identified Index Terms for Browsing Electronic Documents

1. Abstract We present an evaluation of domainindependent natural language tools for use in the identification of significant concepts in documents. Using qualitative evaluation, we compare three shallow processing methods for extracting

Ontological Text Mining of Software Documents

Documents written in natural languages constitute a major part of the software engineering lifecycle artifacts. Especially during software maintenance or reverse engineering, semantic information conveyed in these documents can provide important knowledge for the software engineer. In this paper, we present a text mining system capable of populating a software ontology with information detected...