Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming

Florian Boudin (1)

1 LS2N - équipe TALN - Traitement Automatique du Langage Naturel

We introduce a global inference model for keyphrase extraction that reduces over-generation errors by weighting sets of keyphrase candidates according to their component words. Our model can be applied on top of any supervised or unsuper-vised word weighting function. Experimental results show a substantial improvement over commonly used word-based ranking approaches.

Domaines: Traitement du texte et du document

Florian Boudin. Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming. ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction, Jul 2015, Pékin, China. ⟨hal-01185669⟩