An automated tool to assist in the understanding of legacy code components can be useful both in the areas of software reuse and software maintenance. Most previous work in this area has concentrated on functionally-oriented code. Whereas object-oriented code has been shown to be inherently more reusable than functionally-oriented code, in many cases the eventual reuse of the object-oriented code was not considered during development. A knowledge-based, natural language processing approach to the automated understanding of object-oriented code as an aid to the reuse of object-oriented code is described. A system, called the PATRicia system (Program Analysis Tool for Reuse) that implements the approach is examined. The natural language processing/information extraction system that comprises a large part of the PATRicia system is discussed and the knowledge-base of the PATRicia system, in the form of conceptual graphs, is described. Reports provided by natural language-generation in the PATRicia system are described.

In this paper we present results concerning the large scale automatic extraction of pragmatic content from email by a system based on a phrase matching approach to speech act detection combined with empirical detection of speech act patterns in corpora. The results show that most speech acts that occur in this corpus can be recognized by the approach. This investigation is supported by analysis of a corpus consisting of 1000 emails.

A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.

Most parsing algorithms require phrases that are to be combined to be either contiguous or marked as being ‘extraposed’. The assumption that phrases which are to be combined will be adjacent to one another supports rapid indexing mechanisms: the fact that in most languages items can turn up in unexpected locations cancels out much of the ensuing efficiency. The current paper shows how ‘out of position’ items can be incorporated directly. This leads to efficient parsing even when items turn up having been right-shifted, a state of affairs which makes Johnson and Kay's (1994) notion of ‘sponsorship’ of empty nodes inapplicable.