United we stand: Using multiple strategies for topic labeling

Antoine Gourru (1) , Julien Velcin (1) , Mathieu Roche (2) , Christophe Gravier (3) , Pascal Poncelet (4)

1 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances
2 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
3 LHC - Laboratoire Hubert Curien
4 ADVANSE - ADVanced Analytics for data SciencE

Topic labeling aims at providing a sound, possibly multi-words, label that depicts a topic drawn from a topic model. This is of the utmost practical interest in order to quickly grasp a topic informa-tional content-the usual ranked list of words that maximizes a topic presents limitations for this task. In this paper, we introduce three new unsupervised n-gram topic labelers that achieve comparable results than the existing unsupervised topic labelers but following different assumptions. We demonstrate that combining topic labelers-even only two-makes it possible to target a 64% improvement with respect to single topic labeler approaches and therefore opens research in that direction. Finally, we introduce a fourth topic labeler that extracts representative sentences, using Dirichlet smoothing to add contextual information. This sentence-based labeler provides strong surrogate candidates when n-gram topic labelers fall short on providing relevant labels, leading up to 94% topic covering.

Domaines: Base de données [cs.DB]

Antoine Gourru, Julien Velcin, Mathieu Roche, Christophe Gravier, Pascal Poncelet. United we stand: Using multiple strategies for topic labeling. NLDB: Natural Language Processing and Information Systems, Jun 2018, Paris, France. pp.352-363, ⟨10.1007/978-3-319-91947-8_37⟩. ⟨lirmm-01910614⟩