论文信息 - SEERLAB: A System for Extracting Keyphrases from Scholarly Documents

SEERLAB: A System for Extracting Keyphrases from Scholarly Documents

We describe the SEERLAB system that participated in the SemEval 2010's Keyphrase Extraction Task. SEERLAB utilizes the DBLP corpus for generating a set of candidate keyphrases from a document. Random Forest, a supervised ensemble classifier, is then used to select the top keyphrases from the candidate set. SEERLAB achieved a 0.24 F-score in generating the top 15 keyphrases, which places it sixth among 19 participating systems. Additionally, SEERLAB performed particularly well in generating the top 5 keyphrases with an F-score that ranked third.

C. Lee Giles | Jian Huang | Pradeep B. Teregowda | Pucktada Treeratpituk

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