Single document keyword extraction via quantifying higher-order structural features of word co-occurrence graph. (September 2019)

• Record Type: Journal Article
• Authors: Chen, Yan; Wang, Jie; Li, Ping; Guo, Peilun
• Is Part Of: Computer speech & language. Volume 57(2019)
• Page Start: 98
• Page End: 107
• Publication Date: 2019-09
• Subjects: Word graph -- Motifs -- Keyword extraction; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2019.01.007
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: A new graph-based measure for keyword extraction is proposed, by lever- aging higher-order structural features (e.g. motifs) of word co-occurrence graph. This method considers the frequency of the words as well as the context information. Do not need corpus but the document itself. This method shows superior performance compared to TextRank and TF- IDF based methods. Abstract: Keyword extraction is a building block of information retrieval. Many techniques have been developed to tackle this problem. However, most of the existing methods suffer from high computational complexity or large corpus dependency, which limits the practical applications. Indeed, given a single document, new visions and strategies are needed for keyword extraction to face the challenges. In this work, we proposed a new graph-based measure for keyword extraction, by leveraging higher-order structural features (e.g. motifs) of word co-occurrence graph. The experiments on real datasets shows superior performance of the proposed method, compared to TF-IDF and PageRank based methods.