Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Supertagging: an approach to almost parsing
Computational Linguistics
Vector-based natural language call routing
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A simple named entity extractor using AdaBoost
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The AT&T spoken language understanding system
IEEE Transactions on Audio, Speech, and Language Processing
IXIR: A statistical information distillation system
Computer Speech and Language
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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.