Keyword Extraction Based on PageRank
Lecture notes in computer sciencePublished 20 June 2007
Jinghua Wang, Jianyi Liu, Cong Wang
Citations72
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A keyword extraction algorithm based on WordNet and PageRank that applies UW-PageRank in the rough graph to do word sense disambiguation, prune the graph, and finally apply UW- PageRank again on the pruned graph to extract keywords.
Abstract
Keywords are viewed as the words that represent the topic and the content of the whole text. Keyword extraction is an important technology in many areas of document processing, such as text clustering, text summarization, and text retrieval. This paper provides a keyword extraction algorithm based on WordNet and PageRank. Firstly, a text is represented as a rough undirected weighted semantic graph with WordNet, which defines synsets as vertices and relations of vertices as edges, and assigns the weight of edges with the relatedness of connected synsets. Then we apply UW-PageRank in the rough graph to do word sense disambiguation, prune the graph, and finally apply UW-PageRank again on the pruned graph to extract keywords. The experimental results show our algorithm is practical and effective.
Keywords
Computer Science
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