Paper: Multi-Objective Search Results Clustering

ACL ID C14-1011
Title Multi-Objective Search Results Clustering
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

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 ? -measure and F b 3-measure over two gold standard data sets.