2018/01/05 by Corina Florescu, Wei Jin, Florescu, Corina +1
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1801.01768
To appear in AAAI 2018 Student Abstract and Poster Program
arxiv created 2018/01/05 · openalex publication_date 2018/01/05 · arxiv updated 2018/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.