Situation theory has been developed over the last decade and various versions of the theory have been applied to a number of linguistic issues. However, not much work has been done in regard to its computational aspects. In this paper, we review the existing approaches towards 'computational situation theory' with considerable emphasis on our own research.

This paper develops a general approach to contextual reasoning in natural language processing. Drawing on the view of natural language interpretation as abduction (Hobbs et al., 1993), we propose that interpretation provides an explanation of how an utterance creates a new discourse context in which its interpreted content is both true and promi- nent. Our framework uses dynamic theories of semantics and pragmatics, formal theories of context, and models of attentional state. We describe and illustrate a Prolog implementation.

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.

We translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to exploit the performance of off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action.

Uses connectionism (neural networks) to extract the "gist" of a story in order to represent a context going forward for the disambiguation of incoming words as a text is processed.

An essential ingredient of language use is our ability to reason about utterances as intentional actions. Linguistic representations are the natural substrate for such reasoning, and models from computational semantics can often be seen as providing an infrastructure to carry out such inferences from rich and accurate grammatical descriptions. Exploring such inferences offers a productive pragmatic perspective on problems of interpretation, and promises to leverage semantic representations in more flexible and more general tools that compute with meaning.