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Corina Florescu

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2 papers

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AAAI Conference 2018 Short Paper

Learning Feature Representations for Keyphrase Extraction

Corina Florescu
Wei Jin

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 manuallydesigned 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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AAAI Conference 2017 Short Paper

A Position-Biased PageRank Algorithm for Keyphrase Extraction

Corina Florescu
Cornelia Caragea

Given the large amounts of online textual documents available these days, e. g. , news articles and scientiﬁc papers, effective methods for extracting keyphrases, which provide a highlevel topic description of a document, are greatly needed. We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word’s occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.

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