BBN PLUM: MUC-4 test results and analysis

ABSTRACT

Our mid-term to long-term goals in data extraction from text for the next one to three years are to achieve much greater portability to new languages and new domains, greater robustness, and greater scalability. The novel aspect to our approach is the use of learning algorithms and probabilistic models to learn the domain-specific and language-specific knowledge necessary for a new domain and new language. Learning algorithms should contribute to scalability by making it feasible to deal with domains where it would be infeasible to invest sufficient human effort to bring a system up. Probabilistic models can contribute to robustness by allowing for words, constructions, and forms not anticipated ahead of time and by looking for the most likely interpretation in context.

REFERENCES

• 1: Damaris Ayuso , Sean Boisen , Heidi Fox , Herb Gish , Robert Ingria , Ralph Weischedel, BBN: description of the PLUM system as used for MUC-4, Proceedings of the 4th conference on Message understanding, June 16-18, 1992, McLean, Virginia [doi>10.3115/1072064.1072091]
• 2: Ralph Weischedel , Damaris Ayuso , R. Bobrow , Sean Boisen , Robert Ingria , Jeff Palmucci, Partial parsing: a report on work in progress, Proceedings of the workshop on Speech and Natural Language, p.204-209, February 19-22, 1991, Pacific Grove, California [doi>10.3115/112405.112437]
• 3: Weischedel, R., Meteer, M., and Schwartz, Applications of Statistical Language Modelling to Natural Language Processing, unpublished manuscript, 1991b.