Using Semantic and Syntactic Graphs for Call Classification

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.

[1]  Walter Daelemans,et al.  Improving Accuracy in word class tagging through the Combination of Machine Learning Systems , 2001, CL.

[2]  Robert E. Schapire,et al.  AT&t help desk , 2002, INTERSPEECH.

[3]  Gökhan Tür,et al.  Combining words and prosody for information extraction from speech , 1999, EUROSPEECH.

[4]  Mitchell P. Marcus,et al.  Adding Semantic Annotation to the Penn TreeBank , 1998 .

[5]  Xavier Carreras,et al.  A Simple Named Entity Extractor using AdaBoost , 2003, CoNLL.

[6]  Kenneth Ward Church.  A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text , 1988, ANLP.

[7]  Michael Collins,et al.  Head-Driven Statistical Models for Natural Language Parsing , 2003, CL.

[8]  SchwartzRichard,et al.  Coping with ambiguity and unknown words through probabilistic models , 1993 .

[9]  Daniel Jurafsky,et al.  Semantic Role Labeling by Tagging Syntactic Chunks , 2004, CoNLL.

[10]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[11]  Gökhan Tür,et al.  The AT&T spoken language understanding system , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Richard M. Schwartz,et al.  An Algorithm that Learns What's in a Name , 1999, Machine Learning.

[13]  Xavier Carreras,et al.  Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling , 2004, CoNLL.

[14]  Dilek Z. Hakkani-Tür,et al.  Active and unsupervised learning for automatic speech recognition , 2003, INTERSPEECH.

[15]  Bob Carpenter,et al.  Vector-based Natural Language Call Routing , 1999, Comput. Linguistics.

[16]  Giuseppe Riccardi,et al.  How may I help you? , 1997, Speech Commun..

[17]  Srinivas Bangalore,et al.  Supertagging: An Approach to Almost Parsing , 1999, CL.

[18]  Richard M. Schwartz,et al.  Coping with Ambiguity and Unknown Words through Probabilistic Models , 1993, CL.

[19]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[20]  Martha Palmer,et al.  Adding predicate argument structure to the Penn TreeBank , 2002 .

[21]  Eric Brill,et al.  Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.