Computer Science Department
School of Computer Science, Carnegie Mellon University



This paper introduces the design and use of the Simple Language Generator (SLG). SLG allows the user to construct small but interesting stochastic context-free languages with relative ease. Although context-free grammars are convenient for representing natural language syntax, they do not easily support the semantic and pragmatic constraints that make certain combinations of words or structures more likely than others. Context-free grammars for languages involving many interacting constraints can become extremely complex and cannot reasonably be written by hand. SLG allows the basic syntax of a grammar to be specified in context-free form and constraints to be applied atop this framework in a relatively natural fashion. This combination of grammar and constraints is then converted into a standard stochastic context-free grammar for use in generating sentences or in making context dependent likelihood predictions of the sequence of words in a sentence.


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