Partial parsing: a report on work in progress
HLT '91 Proceedings of the workshop on Speech and Natural Language
Computational aspects of discourse in the context of MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Building a generation knowledge source using Internet-accessible newswire
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
BBN: description of the PLUM system as used for MUC-5
MUC5 '93 Proceedings of the 5th conference on Message understanding
BBN PLUM: MUC-4 test results and analysis
MUC4 '92 Proceedings of the 4th conference on Message understanding
BEN: description of the PLUM system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Progress in information extraction
TIPSTER '96 Proceedings of a workshop on held at Vienna, Virginia: May 6-8, 1996
BBN's PLUM Probabilistic Language Understanding system
TIPSTER '93 Proceedings of a workshop on held at Fredericksburg, Virginia: September 19-23, 1993
Adaptive information extraction
ACM Computing Surveys (CSUR)
How can information extraction ease formalizing treatment processes in clinical practice guidelines?
Artificial Intelligence in Medicine
Wrap-Up: a trainable discourse module for information extraction
Journal of Artificial Intelligence Research
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted 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.