BBN: description of the PLUM system as used for MUC-5

Authors:
Ralph Weischedel;Damaris Ayuso;Sean Boisen;Heidi Fox;Robert Ingria;Tomoyoshi Matsukawa;Constantine Papageorgiou;Dawn MacLaughlin;Masaichiro Kitagawa;Tsutomu Sakai;June Abe;Hiroto Hosihi;Yoichi Miyamoto;Scott Miller
Affiliations:
BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA;BBN Systems and Technologies, Cambridge, MA
Venue:
MUC5 '93 Proceedings of the 5th conference on Message understanding
Year:
1993

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Abstract

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 an ARPA-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.