cal practice: the enterprise of specifying information about the world for use in computer systems. Knowledge representation as a field also encompasses conceptual results that call practitioners’ attention to important truths about the world, mathematical results that allow practitioners to make these truths precise, and computational results that put these truths to work. This chapter surveys this practice and its results, as it applies to the interpretation of natural language utterances in implemented natural language processing systems. For a broader perspective (...) on such technical practice, in all its strengths and weaknesses, see (Agre 1997). Knowledge representation offers a powerful general tool for the science of language. Computational logic, a prototypical formalism for representing knowledge about the world, is also the model for the level of logical form that linguists use to characterize the grammar of meaning (Larson and Segal 1995). And researchers from (Schank and Abelson 1977) to (Shieber 1993) and (Bos to appear) have relied crucially on such representations, and the inference methods associated with them, in articulating accounts of semantic relations in language, such as synonymy, entailment, informativeness and contradiction. The new textbooks (Blackburn and Bos 2002a, Blackburn and Bos 2002b) provide an excellent grounding in this research, and demonstrate how deeply computational ideas from knowledge representation can inform pure linguistic study. In this short chapter, I must leave much of.. (shrink)

Algorithms for NLG NLG is typically broken down into stages of discourse planning (to select information and organize it into coherent paragraphs), sentence planning (to choose words and structures to fit information into sentence-sized units), and realization (to determine surface form of output, including word order, morphology and final formatting or intonation). The SPUD system combines the generation steps of sentence planning and surface realization by using a lexicalized grammar to construct the syntax and semantics of a sentence simultaneously.

This paper presents a simple and versatile tree-rewriting lexicalized grammar formalism, TAGLET, that provides an effective scaffold for introducing advanced topics in a survey course on natural language processing (NLP). Students who implement a strong competence TAGLET parser and generator simultaneously get experience with central computer science ideas and develop an effective starting point for their own subsequent projects in data-intensive and interactive NLP.