@article{Florescu_Jin_2018, title={Learning Feature Representations for Keyphrase Extraction}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/12144}, DOI={10.1609/aaai.v32i1.12144}, abstractNote={ <p> 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. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Florescu, Corina and Jin, Wei}, year={2018}, month={Apr.} }