An Iterative Approach to Keywords Extraction

It is fundamental and important task to extract keyword form documents. Existing methods mainly use statistical or linguistic information to extract the most salient keyword from document. However, those methods ignore the relationship between different granularities (i.e., relationship between word, sentence, and topic). In order to capture and make better use of their relationships between these granularities, this paper proposed an iterative approach for keyword extraction. The method is first implemented by constructing a graph which reflect relationship between different size of granularity nodes, and then using iterative algorithm to calculate score of keywords. Finally, highest score of words in the document will be chosen as keywords. Experimental results show that our approach outperforms baseline methods.

Authors and Affiliations

1. Network Information Center, Shanxi Noraml University, Shanxi, Linfen, 041004, China

   Yang Wei

Editors and Affiliations

1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

   Ying Tan

2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

   Yuhui Shi

3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

   Zhen Ji

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