Abstract
Keyphrase extraction is essential for many IR and NLP tasks. Existing methods usually use the phrases of the document separately without distinguishing the potential semantic correlations among them, or other statistical features from knowledge bases such as WordNet and Wikipedia. However, the mutual semantic information between phrases is also important, and exploiting their correlations may potentially help us more effectively extract the keyphrases. Generally, phrases in the title are more likely to be keyphrases reflecting the document topics, and phrases in the body are usually used to describe the document topics. We regard the relation between the title phrase and body phrase as a description relation. To this end, this paper proposes a novel keyphrase extraction approach by exploiting massive description relations. To make use of the semantic information provided by the description relations, we organize the phrases of a document as a description graph, and employ various graph-based ranking algorithms to rank the candidates. Experimental results on the real dataset demonstrate the effectiveness of the proposed approach in keyphrase extraction.
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Wang, F., Wang, Z., Wang, S., Li, Z. (2014). Exploiting Description Knowledge for Keyphrase Extraction. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_11
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DOI: https://doi.org/10.1007/978-3-319-13560-1_11
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