Multi-Objective Search Results Clustering

Sudipta Acharya (1) , Sriparna Saha (1) , José G. Moreno (2) , Gaël Dias (2)

1 IITP - Indian Institute of Technology Patna
2 Equipe Hultech - Laboratoire GREYC - UMR6072

Most web search results clustering (SRC) strategies have predominantly studied the definition of adapted representation spaces to the detriment of new clustering techniques to improve perfor-mance. In this paper, we define SRC as a multi-objective optimization (MOO) problem to take advantage of most recent works in clustering. In particular, we define two objective functions (compactness and separability), which are simultaneously optimized using a MOO-based simu-lated annealing technique called AMOSA. The proposed algorithm is able to automatically detect the number of clusters for any query and outperforms all state-of-the-art text-based solutions in terms of F β -measure and F b 3 -measure over two gold standard data sets.

Domaines: Traitement du texte et du document

Sudipta Acharya, Sriparna Saha, José G. Moreno, Gaël Dias. Multi-Objective Search Results Clustering. 25th International Conference on Computational Linguistics (COLING 2014), Aug 2014, dublin, Ireland. pp.99 - 108. ⟨hal-01077207⟩