Building Knowledge Bases for the Generation of Software Documentation

Automated text generation requires a underlying knowledge base from which to generate, which is often difficult to produce. Software documentation is one domain in which parts of this knowledge base may be derived automatically. In this paper, we describe \drafter, an authoring support tool for generating user-centred software documentation, and in particular, we describe how parts of its required knowledge base can be obtained automatically.

Learning Micro-Planning Rules for Preventative Expressions

Building text planning resources by hand is time-consuming and difficult. Certainly, a number of planning architectures and their accompanying plan libraries have been implemented, but while the architectures themselves may be reused in a new domain, the library of plans typically cannot. One way to address this problem is to use machine learning techniques to automate the derivation of planning resources for new domains. In this paper, we apply this technique to build micro-planning rules for preventative expressions in instructional text.

A Corpus Study of Negative Imperatives in Natural Language Instructions

In this paper, we define the notion of a preventative expression and discuss a corpus study of such expressions in instructional text. We discuss our coding schema, which takes into account both form and function features, and present measures of inter-coder reliability for those features. We then discuss the correlations that exist between the function and the form features.

Generating Precondition Expressions in Instructional Text

This study employs a knowledge intensive corpus analysis to identify the elements of the communicative context which can be used to determine the appropriate lexical and grammatical form of instructional texts. \ig, an instructional text generation system based on this analysis, is presented, particularly with reference to its expression of precondition relations.