Click-words: learning to predict document keywords from a user perspective.

ABSTRACT:

Motivation

Recognizing words that are key to a document is important for ranking relevant scientific documents. Traditionally, important words in a document are either nominated subjectively by authors and indexers or selected objectively by some statistical measures. As an alternative, we propose to use documents' words popularity in user queries to identify click-words, a set of prominent words from the users' perspective. Although they often overlap, click-words differ significantly from other document keywords.

Results

We developed a machine learning approach to learn the unique characteristics of click-words. Each word was represented by a set of features that included different types of information, such as semantic type, part of speech tag, term frequency-inverse docu

SUBMITTER: Islamaj Dogan R 

PROVIDER: S-EPMC2958742 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature