Unsupervised Keyphrase Extraction with Multipartite Graphs

Florian Boudin 1
1 TALN - Traitement Automatique du Langage Naturel
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.
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Submitted on : Wednesday, January 16, 2019 - 3:21:00 PM
Last modification on : Tuesday, March 26, 2019 - 9:25:22 AM

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Florian Boudin. Unsupervised Keyphrase Extraction with Multipartite Graphs. 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), Jun 2018, Nouvelle Orléans, United States. pp.667 - 672. ⟨hal-01983546⟩

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