GE-CMU: Description of the SHOGUN System Used for MUC-5

GE-CMU: DESCRIPTION OF THE SHOGUN SYSTEM USED FOR MUC-5 Paul S. Jacobs, George Krupka, and Lisa Rau Information Technology Laboratory GE Research and Developmen t Schenectady, NY 12301 USA Michael L . Mauldin, Teruko Mitamura, and Tsuyoshi Kitan i Center for Machine Translation Carnegie Mellon University Pittsburgh, PA 15213 USA Ira Sider and Lois Childs Management Data System s Martin Mariett a Philadelphia, PA 19101 US A Abstract This paper describes the GE-CMU TIPSTER/SHOGUN system as configured for the TIP- STER 24-month (MUC-5) benchmark, and gives details of the system's performance on the se- lected Japanese and English texts. The SHOGUN system is a distillation of some of the key ideas that emerged from previous benchmarks and experiments, emphasizing a simple architecture in which the focus is on detailed corpus-based knowledge . This design allowed the project to meet its goal of achieving advances in coverage and accuracy while showing consistently good performanc e across languages and domains . INTRODUCTION The GE-CMU TIPSTER/SHOGUN system is the result of a two-year research effort, part of the A1tPA - sponsored TIPSTER: data extraction program. The project's main goals were : (1) to develop algorithms that would advance the state of the art in coverage and accuracy in data extraction, and (2) to demonstrat e high performance across languages and domains and to develop methods for easing the adaptation of the system to new languages and domains . The system as used in MUC-5 represents a considerable shift from those used in earlier stages of th e program and in previous MUC 's. The original SHOGUN design integrated several different approaches b y combining different knowledge sources, such as syntax, semantics, phrasal rules, and domain knowledge, a t run-time. This allowed the system to achieve a good level of performance very quickly, and made it easy t o test different modules and methods ; however, it proved very difficult to make all the changes necessary t o improve the system, especially across languages, when system knowledge was so distributed at run-time . As a result, the team adopted a new approach, relying heavily on finite-state approximation . This method combines several earlier previous of work, including Pereira's research on grammar approximation [4], som e of the original ideas on parser compilation from Tomita [5], and GE 's representation of the dynamic lexico n [3, 1] . Like Pereira 's model, the system uses a finite-state grammar as a loose version of a context fre e 1 This research was sponsored (in part) by the Advanced Research Project Agency (DOD) and other government agencies . The views and conclusions contained in this document are those of the authors and should not be interpreted as representin g the official policies, either expressed or implied, of the Advanced Research Project Agency or the US Government . 109 grammar, under the assumption that the finite state grammar will cover all the inputs that the genera l grammar would recognize but perhaps be more tolerant . However, the …

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