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This paper describes a computational approach to certain problems of anaphora in natural language and argues in favor of formal meaning representation languages (MRLs) for natural language. After presenting arguments in favor of formal meaning representation languages, appropriate MRLs are discussed. Minimal requirements include provisions for constructions equivalent to lambda expressions and for the separation of descriptional and assertional information types. A series of examples is discussed, in order to insure that, (1) in cases where the antecedent of an anaphoric expression is not explicitly present, it can often be derived through purely syntactic manipulations of an appropriately structured MRL and (2) the properties proposed for a logical MRL make such manipulations simple to express and to apply. Given an appropriate representation language, it is possible to deal with a rich class of anaphora. (AA)

Current natural language understanding systems generally maintain a strict division between the parsing processes and the representation that supports general reasoning about the world. This paper examines why these two forms of processing are separated, determines the current advantages and limitations of this approach, and identifies the inherent limitations of the approach. I will point out some fundamental problems with the models as they are defined today and suggest some important directions of research in natural language and knowledge representation. In particular, I will argue that one of the crucial issues facing future natural language systems is the development of knowledge representation formalisms that can effectively handle ambiguity.