A Unified Probabilistic Approach for Semantic Clustering of Relational Phrases
Adam Grycner, Gerhard Weikum, Jay M. Pujara, James R. Foulds, Lise Getoor · MPG.PuRe (Max Planck Society) · 2014
The task of finding synonymous relational phrases is important in natural lan-guage understanding problems such as question answering and paraphrase detec-tion. While this task has been addressed by many previous systems, each of these existing approaches is limited either in expressivity or in scalability. To address this challenge, we present a large-scale statistical relational method for clustering relational phrases using Probabilistic Soft Logic (PSL) [1]. To assess the quality of our approach, we evaluated it relative to a set of baseline methods. The pro-posed technique was found to outperform the baselines for both clustering and link prediction, and was shown to be scalable enough to be applied to 200,000 relational phrases. 1