HLT '89 Proceedings of the workshop on Speech and Natural Language
Partial parsing: a report on work in progress
HLT '91 Proceedings of the workshop on Speech and Natural Language
Studies in part of speech labelling
HLT '91 Proceedings of the workshop on Speech and Natural Language
Towards understanding text with a very large vocabulary
HLT '90 Proceedings of the workshop on Speech and Natural Language
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
MITRE-Bedford: description of the ALEMBIC system as used for MUC-4
MUC4 '92 Proceedings of the 4th conference on Message understanding
Adaptive information extraction
ACM Computing Surveys (CSUR)
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Traditional approaches to the problem of extracting data from texts have emphasized handcrafted linguistic knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of a DARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques. Our research and development goals are• more rapid development of new applications,• the ability to train (and re-train) systems based on user markings of correct and incorrect output,• more accurate selection among interpretations when more than one is found, and• more robust partial interpretation when no complete interpretation can be found.