Learning Feature Representations for Keyphrase Extraction


  • Corina Florescu University of North Texas
  • Wei Jin University of North Texas




keyphrase extraction, keyphrase embeddings, feature learning, graph representation


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.




How to Cite

Florescu, C., & Jin, W. (2018). Learning Feature Representations for Keyphrase Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12144