Many of the more ambitious goals of artificial intelligence have proved unattainable because of the failure of the many small, successful systems to scale up. The general use of technologies such as natural language interfaces and expert systems has done little to alleviate the basic difficulties and overwhelming cost of knowledge engineering. At the same time, emerging text processing techniques, including data extraction from text and new text retrieval methods, offer a means of accessing stores of information many times larger than any organized knowledge base or database. Although knowledge acquisition from text is at the heart of the information management problem, interpreting text, paradoxically, requires large amounts of knowledge, mainly about the way words are used in context. In other words, before intelligent text processing systems can be trained to mine for useful knowledge, they must already have enough knowledge to interpret what they read. The point at which there is "enough", is still a matter of debate, as no real program seems close to having enough knowledge to achieve general human-like understanding. Current research in large-scale natural language processing has come, rightly, to focus on lexical acquisition as the key to future progress. Unfortunately, the current state of the art is quite far from the recipe for acquiring knowledge about words, because it leans too heavily on resources that are available, without consideration for what is needed.>

Transportability has perpetually been the nemesis of natural language processing systems, in both the research and commercial sectors. During the last 20 years, the technology has not moved much closer to providing robust coverage of everyday language, and has failed to produce commercial successes beyond a few specialized interfaces and application programs. the redesign required for each application has limited the impact of natural language systems. Trump (TRansportable Understanding Mechanism Package) is a natural language analyzer that functions in a variety of domains, in both interfaces and text processing. While other similar efforts have treated transportability as a problem in knowledge engineering, Trump instead relies mainly on a “core” of knowledge about language and a set of techniques for applying that knowledge within a domain. the information about words, word meanings, and linguistic relations in this generic knowledge base guides the conceptual framework of language interpretation in each domain. Turmp uses this core knowledge to piece together a conceptual representation of a natural language input by combining generic and specialized inforamtion. the result has been a language processing system that is capable of performing fairly extensive analysis with a minimum of customization for each application.

Indexing text for accurate retrieval ZS a dificuli and in)portant problem.. On-ltne information services generally depend on "keyword" mdtces rather ihat~other methods of retrieval, because of the pract~cal jealures of keywords for storage, dtssemtnatlonj and browsing m well as for

Neither natural language processing nor information retrieval is any longer a young field, but the two areas have yet to achieve a graceful interaction. Mainly, the reason for this incompatibility is that information retrieval technology depends upon relatively simple but robust methods, while natural language processing involves complex knowledge-based systems that have never approached robustness. We provide an analysis of areas in which natural language and information retrieval come together, and describe a system that joins the two fields by combining technology, choice of application area, and knowledge acquisition techniques.

Neither natural language processing nor information retrieval is any longer a young field, but the two areas have yet to achieve a graceful interaction. Mainly, the reason for this incompatibility is that information retrieval technology depends upon relatively simple but robust methods, while natural language processing involves complex knowledge-based systems that have never approached robustness. We provide an analysis of areas in which natural language and information retrieval come together, and describe a system that joins the two fields by combining technology, choice of application area, and knowledge acquisition techniques.

Approaches to natural language processing that use a phrasal lexicon have the advantage of easily handling linguistic constructions that might otherwise be extragrammatical. However, current phrasal lexicons are often too rigid: their phrasal entries fail to cover the more flexible constructions. FLUSH, for Flexible Lexicon Utilizing Specialized and Hierarchical knowledge, is a knowledge-based lexicon design that allows broad phrasal coverage.