Keyword extraction based on pagerank

Authors:
Jinghua Wang;Jianyi Liu;Cong Wang
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

Citing 9
Cited 5

Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone

SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet

CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
The role of domain information in Word Sense Disambiguation

Natural Language Engineering
Verbs semantics and lexical selection

ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Semantic document engineering with WordNet and PageRank

Proceedings of the 2005 ACM symposium on Applied computing
Graph-based ranking algorithms for sentence extraction, applied to text summarization

ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
PageRank on semantic networks, with application to word sense disambiguation

COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Combining knowledge- and corpus-based word-sense-disambiguation methods

Journal of Artificial Intelligence Research

PageRank for ranking authors in co-citation networks

Journal of the American Society for Information Science and Technology
Discovering author impact: A PageRank perspective

Information Processing and Management: an International Journal
SemanticRank: ranking keywords and sentences using semantic graphs

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Advertising keywords extraction from web pages

WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Keyword extraction based on sequential pattern mining

Proceedings of the Third International Conference on Internet Multimedia Computing and Service

Quantified Score

Hi-index	0.00

Visualization

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.