A DISCRETE PARTICLE SWARM OPTIMIZER FOR CLUSTERING SHORT-TEXT CORPORA
Leticia Cagnina, Marcelo Luis Errecalde, Diego Alejandro Ingaramo, Paolo Rosso · 2013
Work on “short-text clustering ” is relevant, particularly if we consider the current/future mode for people to use ‘small-language’, e.g. blogs, text-messaging, snippets, etc. Potential applications in different areas of natural language processing may include re-ranking of snippets in information retrieval, and automatic clustering of scientific texts available on the Web. Despite its relevance, this kind of problems has not received too much attention by the computational linguistic community due to the high challenge that this problem implies. In this work, we propose the CLUDIPSO algorithm, a novel approach for clustering short-text collections based on a discrete Particle Swarm Optimizer. Our approach explicitly considers clustering as an optimization problem where a given arbitrary objective function must be optimized. We used two unsupervised measures of cluster validity with this purpose: the Expected Density Measure and the Global Silhouette coefficient. These measures have shown interesting results in recent works on short-text clustering. The results indicate that our approach is a highly competitive alternative to solve this kind of problems.