Chris Mellish

Advances in Instance Selection for Instance-Based Learning Algorithms

The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.

ILEX: an architecture for a dynamic hypertext generation system

Generating text in a hypermedia environment places different demands on a text generation system than occurs in non-interactive environments. This paper describes some of these demands, then shows how the architecture of one text generation system, ILEX, has been shaped by them. The architecture is described in terms of the levels of linguistic representation used, and the processes which map between them. Particular attention is paid to the processes of content selection and text structuring.

AUTOMATIC-GENERATION OF TECHNICAL DOCUMENTATION

Natural-language generation (NLG) techniques can be used to automatically produce technical documentation from a domain knowledge base and linguistic and contextual models. We discuss this application of NLG technology from both a technical and a usefulness (costs and benefits) perspective. This discussion is based largely on our experiences with the Intelligent Documentation Advisory System (IDAS) documentation-generation project, and the reactions that various interested people from industry have had to IDAS. We hope that this summary of our experiences with IDAS and the lessons we have learned from it will be beneficial for other researchers who wish to build technical documentation-generation systems.

Choosing the content of textual summaries of large time-series data sets

Natural Language Generation (NLG) can be used to generate textual summaries of numeric data sets. In this paper we develop an architecture for generating short (a few sentences) summaries of large (100KB or more) time-series data sets. The architecture integrates pattern recognition, pattern abstraction, selection of the most significant patterns, microplanning (especially aggregation), and realisation. We also describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textualsummaries of sensor data from gas turbines.

SOME CHART-BASED TECHNIQUES FOR PARSING ILL-FORMED INPUT

We argue for the usefulness of an active chart as the basis of a system that searches for the globally most plausible explanation of failure to syntactically parse a given input. We suggest semantics-free, grammar-independent techniques for parsing inputs displaying simple kinds of ill-formedness and discuss the search issues involved.

Conversation in the museum: experiments in dynamic hypermedia with the intelligent labelling explorer

We outline experience with the Intelligent Labelling Explorer, a dynamic hypertext system developed at the University of Edinburgh, in collaboration with the National Museums of Scotland. First, we indicate a number of ways in which labels on museum objects ought to be tuned to take into account types of visit, the interests of visitors, and their evolving knowledge during a visit. Secondly, we sketch the general architecture of our system, and then focus on the conversational effects which the system can create. We then briefly indicate future directions of research, before critically discussing the applicability (or otherwise) of the spatial metaphor to flexible hypertexts.

Risk-taking and recovery in task-oriented dialogue

The Principle of Parsimony states that people usually try to complete tasks with the least effort that will produce a satisfactory solution. In task-oriented dialogue, this produces a tension between conveying information carefully to the partner and leaving it to be inferred, risking a misunderstanding and the need for recovery. Using natural dialogue examples, primarily from the HCRC Map Task, we apply the Principle of Parsimony to a range of information types and identify a set of applicable recovery strategies. We argue that risk-taking and recovery are crucial for efficient dialogue because they pinpoint which information must be transferred and allow control of the interaction to switch to the participant who can best guide the course of the dialogue.

Instance-based natural language generation

This paper presents a bottom-up generator that makes use of Information Retrieval techniques to rank potential generation candidates by comparing them to a data base of stored instances. We introduce two general techniques to address the search problem, expectation-driven search and dynamic grammar rule selection, and present the architecture of an implemented generation system called IGEN. Our approach uses a domain-specific generation grammar that is automatically derived from a semantically tagged treebank. We then evaluate the efficiency of our system.

A Reference Architecture for Natural Language Generation Systems

We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

Automatic Generation of On-Line Documentation in the IDAS Project

The Intelligent Documentation Advisory System generates on-line documentation and help messages from a domain knowledge base, using natural-language (NL) generation techniques. This paper gives an overview of IDAS, with particular emphasis on: (1) its architecture and the types of questions it is capable of answering; (2) its KR and NL generation systems, and lessons we have learned in designing them; and (3) its hypertext-like user interface, and the benefits such an interface brings.