Evaluating Natural Language Systems: A Sourcebook Approach

This paper reports progress in development of evaluation methodologies for natural language systems. Without a common classification of the problems in natural language understanding authors have no way to specify clearly what their systems do, potential users have no way to compare different systems and researchers have no way to judge the advantages or disadvantages of different approaches to developing systems. i n t r o d u c t i o n . Recent years have Seen a proliferation of natural language systems. These include both applied systems such as database front-ends, expert system interfaces and on-line help systems and research systems developed to test particular theories of language processing. Each system comes with a set of claims about what types of problems the system can "handle". But what does "handles ellipsis" or "resolves anaphoric reference" actually mean? All and any such cases? Certain types? And what classification of ' types' of ellipsis is the author using? Without a common classification of the problems in natural language understanding authors have no way to specify clearly what their systems do, potential users have no way to compare different systems and researchers have no way to judge the advantages or disadvantages of different approaches to developing systems. While these problems have been noted over the last 10 years (Woods, 1977; Tennant, 1979), research developing specific criteria for evaluation of natural language systems has appeared only recently. This paper reports progress in development of evaluation methodologies for natural language systems. This work is part of the Artificial Intelligence Measurement System (AIMS) project of the Center for the Study of Evaluation at UCLA. The AIMS project is developing evaluation criteria for expert systems, vision systems and natural language systems. i~revious W o r k on N a t u r a l L a n g u a g e Eva lua t ion . Woods (1977) discussed a number of dimensions along which nro~re~s in development of natural language systems can be *This work reported here is part of the Artificial Intelligence Measurement Systems (AIMS) Project, which is supported in part by ONR contract number N00014-S6-K-0395. measured. In particular, he considered approaches via a %axonomy of linguistic phenomena" covered, the convenience and perspicuity of the model used and the time used in processing. As Woods points out, the difficulty of a taxonomic approach is that the taxonomy will always be incomplete. Any particular phenomenon will have many subclasses and it often turns out tha t the pubhshed examples cover only a small part oZ the problem. A system might claim "handles pronoun reference" but the examples only cover parallel constructions. To make such a taxonomy useful we have to identify as many subclasses as possible. On the positive side, if we can bui ld such a taxonomy, it will allow authors to state clearly just what phenomena they are making claims about. It could serve not only as a description of what has been achieved but as a guide to what still needs to be done. Woods provides a useful discussion of the difficulties involved in each of these approaches but offers no specific evaluative criteria. He draws attention to the great effort involved in doing evaluation by any of these methods and to the importance of a "detailed case-by-case analysis". Our present work is an implementation and extension of some of these ideas. Tennant and others (Tennant 1979; Finin, Goodman & Tennant, 1979) make a distinction between conceptual coy. erage and linguistic coverage of a natural language system and argue that systems have to be measured on each of t h ~ e dimensions. Conceptual coverage refers to the range of concepts handled by the system and linguistic coverage to the range of language used to discuss the concepts. Tennant suggests a possible experimental separation between conceptual and linguistic coverage. The distinction these authors make is important and useful, in part for emphasizing the significance of the knowledge base for usability of a natm'al language system. But the examples that Tennant gives for conceptual completeness presupposition, reference to discourse objects seem to be