DATR: A Language for Lexical Knowledge Representation

Much recent research on the design of natural language lexicons has made use of nonmonotonic inheritance networks as originally developed for general knowledge representation purposes in Artificial Intelligence. DATR is a simple, spartan language for defining nonmonotonic inheritance networks with path/value equations, one that has been designed specifically for lexical knowledge representation. In keeping with its intendedly minimalist character, it lacks many of the constructs embodied either in general-purpose knowledge representation languages or in contemporary grammar formalisms. The present paper shows that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of linguistic analysis. The paper provides an informal example-based introduction to DATR and to techniques for its use, including finite-state transduction, the encoding of DAGs and lexical rules, and the representation of ambiguity and alternation. Sample analysis of phenomena such as inflectional syncretism and verbal subcategorization are given that show how the language can be used to squeeze out redundancy from lexical descriptions.

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