Semantic Kernels for Semantic Parsing

We present an empirical study on the use of semantic information for Concept Seg- mentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative analyses out- put by a state-of-the-art CSL parser with tree structures, which we rerank with a classifier trained on two types of seman- tic tree kernels: one processing structures built with words, concepts and Brown clusters, and another one using semantic similarity among the words composing the structure. The results on a corpus from the restaurant domain show that our semantic kernels exploiting similarity measures out- perform state-of-the-art rerankers.