Ensemble learning for keyphrases extraction from scientific document

Authors:
Jiabing Wang;Hong Peng;Jing-song Hu;Jun Zhang
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;School of Information Science, Guangdong Commerce College, Guangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Keyphrase extraction is a task with many applications in information retrieval, text mining, and natural language processing. In this paper, a keyphrase extraction approach based on neural network ensemble is proposed. To determine whether a phrase is a keyphrase, the following features of a phrase in a given document are adopted: its term frequency, whether to appear in the title, abstract or headings (subheadings), and its frequency appearing in the paragraphs of the given document. The approach is evaluated by the standard information retrieval metrics of precision and recall. Experiment results show that the ensemble learning can significantly increase the precision and recall.