Contextual Inference in Computational Semantics

TL;DR

This paper introduces a method that combines formal context modeling with automated theorem proving to efficiently compute presuppositions in natural language discourse, improving inference processes in computational semantics.

Contribution

It presents an integrated approach using nested discourse representations and a tableau calculus to enhance inference efficiency in computational semantics.

Findings

Efficient computation of presuppositions using nested contexts

Reduction of redundant inference steps

Explicit context nesting improves inference accuracy

Abstract

In this paper, an application of automated theorem proving techniques to computational semantics is considered. In order to compute the presuppositions of a natural language discourse, several inference tasks arise. Instead of treating these inferences independently of each other, we show how integrating techniques from formal approaches to context into deduction can help to compute presuppositions more efficiently. Contexts are represented as Discourse Representation Structures and the way they are nested is made explicit. In addition, a tableau calculus is present which keeps track of contextual information, and thereby allows to avoid carrying out redundant inference steps as it happens in approaches that neglect explicit nesting of contexts.