Distributed Feature Sets for Document Specific Key-Phrase Extraction

Abstract

The need for knowledge and the satisfaction of this need is the fastest growing market in the world. Among all types of data, the textual documents are an incredible source of knowledge. In order not to lose track of this rapid development of information diversity, it is necessary to provide schemes that make it possible to filter out and present specific information from this huge amount of textual data. Since then, key-phrase extraction methods have started to gain importance. A key-phrase extraction system provides a selection of relevant data in textual documents based on languages and corresponding extraction rules. To extract the important concepts from the documents, the system should be able to use special features and self-identify properties of the words in the texts and properties of the documents. In this article, we developed and discussed different sets of features which are unrestricted to form, size and organization of the documents, i.e. a novel set of regular, advanced and external knowledge-based features are proposed. To selectively combine the best features from all three sets of features here we deploy the two different automatic feature selection techniques. Four different datasets are used here to evaluate the performance of the individual, combined and best selected features. The dynamic programming-based feature selection approaches significantly improves the performance in contrast to the different feature sets (individual and combined both) and state-of-the-art.