Diverse feature set based Keyphrase extraction and indexing techniques

Abstract

The internet changed the way that people communicate, and this has led to a vast amount of Text that is available in electronic format. It includes things like e-mail, technical and scientific reports, tweets, physician notes and military field reports. Providing key-phrases for these extensive text collections thus allows users to grab the essence of the lengthy contents quickly and helps to locate information with high efficiency. While designing a Keyword Extraction and Indexing system, it is essential to pick unique properties, called features. In this article, we proposed different unsupervised keyword extraction approaches, which is independent of the structure, size and domain of the documents. The proposed method relies on the novel and cognitive inspired set of standard, phrase, word embedding and external knowledge source features. The individual and selected feature results are reported through experimentation on four different datasets viz. SemEval, KDD, Inspec, and DUC. The selected (feature selection) and word embedding based features are the best features set to be used for keywords extraction and indexing among all mentioned datasets. That is the proposed distributed word vector with additional knowledge improves the results significantly over the use of individual features, combined features after feature selection and state-of-the-art. After successfully achieving the objective of developing various keyphrase extraction methods we also experimented it for document classification task.