Learning Algorithms for Keyphrase Extraction
Information Retrieval
Using Noun Phrase Heads to Extract Document Keyphrases
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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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.