Learning Feature Representations for Keyphrase Extraction

Corina Florescu; Wei Jin

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arXiv · 1801.01768

Learning Feature Representations for Keyphrase Extraction

Corina Florescu·Wei Jin

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Abstract

In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases. Although the manually-designed features have shown to work well in practice, feature engineering is a difficult process that requires expert knowledge and normally does not generalize well. In this paper, we present SurfKE, a feature learning framework that exploits the text itself to automatically discover patterns that keyphrases exhibit. Our model represents the document as a graph and automatically learns feature representation of phrases. The proposed model obtains remarkable improvements in performance over strong baselines.

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Corina Florescu, Wei Jin. 2018-01-05. Learning Feature Representations for Keyphrase Extraction. https://arxiv.org/abs/1801.01768

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