BBN's PLUM Probabilistic Language Understanding system

Authors:
Ralph Weischedel;Damaris Ayuso;Sean Boisen;Heidi Fox;Tomoyoshi Matsukawa;Constantine Papageorgiou;Dawn MacLaughlin;Masaichiro Kitawa;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
Venue:
TIPSTER '93 Proceedings of a workshop on held at Fredericksburg, Virginia: September 19-23, 1993
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:• Achieving high performance in objective evaluations, such as the Tipster evaluations.• Reducing human effort in porting the natural language algorithms to new domains and to new languages.• Providing technology that is scalable to realistic applications.