Introduction to the theory of neural computation
Introduction to the theory of neural computation
Stochastic representation of conceptual structure in the ATIS task
HLT '91 Proceedings of the workshop on Speech and Natural Language
Fundamentals of speech recognition
Fundamentals of speech recognition
Document and passage retrieval based on hidden Markov models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Language Learning
Improved Topic Discrimination of Broadcast News Using a Model of Multiple Simultaneous Topics
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Data extraction as text categorization: an experiment with the MUC-3 corpus
MUC3 '91 Proceedings of the 3rd conference on Message understanding
The generic information extraction system
MUC5 '93 Proceedings of the 5th conference on Message understanding
Design of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
On the marriage of information retrieval and information extraction
IRSG'97 Proceedings of the 19th Annual BCS-IRSG conference on Information Retrieval Research
Sequence models for automatic highlighting and surface information extraction
IRSG'99 Proceedings of the 21st Annual BCS-IRSG conference on Information Retrieval Research
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We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the most relevant phrases, and labels the type of information they contain. The approach and preliminary results are demonstrated on a subset of the MUC-6 Scenario Templates task.