Keyphrases extraction from Web document by the least squares support vector machine

Automatic keyphrase extraction from documents is a task with many applications in information retrieval and natural language processing. Previously, several keyphrase extraction methods have been proposed based on different techniques. In this paper, a keyphrase extraction algorithm based on the least squares support vector machine is proposed. In order to determine whether a phrase is a keyphrase or not, the following features of a phrase in a given document are adopted: its TF (term frequency) and IDF (inverted document frequency), whether or not it appears in the title or headings (subheadings) of the given document, and its distribution in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall and human assessment. Experiment results show that this approach is competitive with other known methods.