We present an empirical study on the use of semantic information for Concept Segmentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative analyses output by a state-of-the-art CSL parser with tree structures, which we rerank with a classifier trained on two types of semantic 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 outperform state-of-the-art rerankers.
Semantic Kernels for Semantic Parsing
Iman Saleh, Alessandro Moschitti, Preslav Nakov, Lluís Màrquez, and Shafiq Joty. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP'14) , pages 436-442, 2014.
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