Paper: Detecting Multiple Facets of an Event using Graph-Based Unsupervised Methods

ACL ID C08-1077
Title Detecting Multiple Facets of an Event using Graph-Based Unsupervised Methods
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

We propose a new unsupervised method for topic detection that automatically iden- tifies the different facets of an event. We use pointwise Kullback-Leibler divergence along with the Jaccard coefficient to build a topic graph which represents the com- munity structure of the different facets. The problem is formulated as a weighted set cover problem with dynamically vary- ing weights. The algorithm is domain- independent and generates a representa- tive set of informative and discriminative phrases that cover the entire event. We evaluate this algorithm on a large collec- tion of blog postings about different news events and report promising results.