Book Reviews: Interpreting Anaphors in Natural Language Texts

This book explores an approach to text understanding, in particular anaphora resolution, that tries to limit the use of detailed domain knowledge and commonsense inference by exploiting general linguistic knowledge as much as possible. Carter proposes that, because natural language texts are relatively redundant and are constructed considerately, it should be possible in many cases to recover the interpretation of the text by using either linguistic or nonlinguistic techniques. Linguistic techniques are preferable because they are more general and less open-ended. Carter's approach is called "shallow processing. " Since domain knowledge and reasoning tend to be expensive to implement, maintain, and use during processing, the shallow processing approach should be much more efficient and portable than an approach based heavily on domain-specific knowledge. If it can at the same time provide reasonable accuracy, then it should be very useful. This seems to be a very sensible approach in principle, and Carter demonstrates that it is quite effective in practice. The shallow processing hypothesis is tested in a program called SPAR (Shallow Processing Anaphor Resolver), which was implemented as part of the author's University of Cambridge thesis. Shallow processing is presented as an engineering solution to the problem of dealing with the use of domain knowledge in text understanding, for certain applications, not as a psychological hypothesis. In the SPAR architecture, general linguistic techniques, such as focusing, are used first. Domain reasoning is only used if more than one candidate referent remains after the application of linguistic knowledge. Although this approach is tested specifically only for reference resolution, obviously it could be extended to other areas of natural language processing in which both linguistic and domain knowledge could be used, such as reasoning. The book presents an excellent and very clear review of both current and older approaches to anaphora resolution, as well as a clear description of the SPAR system. For this reason, it would serve as a very good text for a seminar on reference resolution as well as an extra reading for a class on knowledge representation. One attractive aspect of the SPAR system is that it builds on previous work where appropriate, and extends it where required. In particular, it integrates the work of Boguraev (1979) in parsing, the work of Sidner (1979) in focusing, and the work of Wilks (1975) in preference semantics. Where Sidner's work, for example, is incomplete, as in the treatment of intrasentential anaphora, Carter presents a reasonable extension to handle the additional phenomena. One minor oversight in this work is that, in the treatment of one-anaphora, Carter fails to explore recent pragmatically oriented approaches, such as those discussed by Webber (1983) and Dahl (1984), who propose unified treatments of definite pronouns and one-anaphora. Instead, SPAR uses the older, and probably less effective, syntactic approach suggested by Webber (1978) and Halliday and Hasan (1976). Another commendable aspect of this work is that Carter presents specific statistics on the accuracy of his system-93% of pronominal anaphors (out of 242) and 82% of nonpronominal anaphors (out of 80) are resolved correctly. Although these statistics go beyond what is usually reported, it would have been even more interesting to see a detailed breakdown of anaphor types and accuracy. It would also have been interesting to see statistics on the efficiency of the system, since the overall algorithm is quite complex. This high level of accuracy provides evidence that shallow processing is a promising approach. However, these statistics do raise the issue that we don't really know what level of accuracy in anaphora resolution is "good enough. " In fact, the "good enough" level of accuracy may vary by application. Ninety-three percent may be accurate enough for some applications, such as machine translation, or message routing, but not for others, such as database update. Perhaps the relatively inexpensive shallow processing approach will turn out to be the method of choice for applications with lower accuracy requirements. A related issue that this work raises is how accurate we can expect to get-that is, how good is human performance on anaphora resolution, and how close is Carter's system to that level? These are questions that we simply don't know the answers to, and that await future research, Carter has done a valuable service in providing his statistics, but it is difficult to interpret them without having these bases of comparison and without having comparable statistics from other approaches. In addition to supporting the shallow processing hypothesis, this work also supports the usefulness (at least from an