TY - JOUR AU - Florescu, Corina AU - Jin, Wei PY - 2018/04/29 Y2 - 2024/03/28 TI - Learning Feature Representations for Keyphrase Extraction JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Student Abstract Track DO - 10.1609/aaai.v32i1.12144 UR - https://ojs.aaai.org/index.php/AAAI/article/view/12144 SP - AB - <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> ER -