Using semantic and syntactic graphs for call classification

Abstract

In this paper, we introduce a new data representation format for language processing, the syntactic and semantic graphs (SSGs), and show its use for call classification in spoken dialog systems. For each sentence or utterance, these graphs include lexical information (words), syntactic information (such as the part of speech tags of the words and the syntactic parse of the utterance), and semantic information (such as the named entities and semantic role labels). In our experiments, we used written language as the training data while computing SSGs and tested on spoken language. In spite of this mismatch, we have shown that this is a very promising approach for classifying complex examples, and by using SSGs it is possible to reduce the call classification error rate by 4.74% relative.