GE: description of the NLTooLSET system as used for MUC-3

The GE NLTooLsET aims at extracting and deriving useful information from text using a knowledge-based , domain-independent core of text processing tools, and customizing the existing programs to each new task . The program achieves this transportability by using a core knowledge base and lexicon that adapts easil y to new applications, along with a flexible text processing strategy that is tolerant of gaps in the program' s knowledge base . The NLTooLSET's design provides each system component with access to a rich hand-coded knowledg e base, but each component applies the knowledge selectively, avoiding the computation that a complete analysis of each text would require . The architecture of the system allows for levels of language analysis , from rough skimming to in-depth conceptual interpretation . The NLTooLSET, in its first version, was behind GE 's participation in the MUCK-II conference . Since MUCK-II, the Toolset, now in Release 2 .1, has expanded to include a number of new capabilities, including a text pre-processor for easier customization and better performance, broader lexical and syntactic coverage , and a domain-independent module for applying word-sense preferences in text . In addition to being teste d in several new application areas, the Toolset has achieved about a 10 times speedup in words per minute s over MUCK-II, and can now partially interpret and tag word senses in arbitrary news stories, although it i s very difficult to evaluate this task-independent performance . These basic enhancements preceded the other additions, including a discourse processing module ;, which were made for MUC-3 . The performance of the program on tasks such as MUCK-II and MUC-3 derives mainly from two design characteristics : central knowledge hierarchies and flexible control strategies . A custom-built 10,000 word-root lexicon and 1000-concept hierarchy provides a rich source of lexical information . Entries are separated b y their senses, and contain special context clues to help in the sense-disambiguation process . A morphologica l analyzer contains semantics for about. 75 affixes, and can automatically derive the meanings of inflecte d entries not separately represented in the lexicon . Domain-specific words and phrases are added to th e lexicon by connecting them to higher-level concepts and categories present in the system 's core lexicon and concept hierarchy. Lexical analysis can also be restricted or biased according to the features of a domain . This is one aspect of the NLTooLSET that makes it highly portable from one domain to another . The language analysis strategy in the NLTooLSET uses fairly detailed, chart-style syntactic parsin g guided by conceptual expectations . Domain-driven conceptual structures provide feedback in parsing, contribute to scoring alternative interpretations, help recovery from failed parses, and tie together information across sentence boundaries . The interaction between linguistic and conceptual knowledge sources at the leve l of linguistic relations, called "relation-driven control" was a key system enhancement before MUC-3 . In addition to flexible control, the design of the NLTooLSET allows each knowledge source to influenc e different stages of processing . For example, discourse processing starts before parsing, although many decisions about template merging and splitting are made after parsing . This allows context to guide languag e analysis, while language analysis still determines context .