Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization

Abstract. We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.

• Type: Journal
• Year: 2015
• Where: NLDB
• Authors: Michael Wiegand, Benjamin Roth, Dietrich Klakow
• Keywords: Natural Language Processing, NLDB 2015