Hierarchical word clustering - automatic thesaurus generation

In this paper, we propose a hierarchical, lexical clustering neural network algorithm that automatically generates a thesaurus (synonym abstraction) using purely stochastic information derived from unstructured text corpora and requiring no prior word classifications. The lexical hierarchy overcomes the Vocabulary Problem by accommodating paraphrasing through using synonym clusters and overcomes Information Overload by focusing search within cohesive clusters. We describe existing word categorisation methodologies, identifying their respective strengths and weaknesses and evaluate our proposed approach against an existing neural approach using a benchmark statistical approach and a human generated thesaurus for comparison. We also evaluate our word context vector generation methodology against two similar approaches to investigate the effect of word vector dimensionality and the effect of the number of words in the context window on the quality of word clusters produced. We demonstrate the effectiveness of our approach and its superiority to existing techniques. (C) 2002 Elsevier Science B.V. All rights reserved

How Changes in Plant Community Structure Affect Ant Communities

We investigated how change in plant community composition brought about by annual grass-specific herbicide application affects terrestrial arthropod communities, with special emphasis on the mutualists of the endangered Fender’s blue butterfly, Plebejus icarioides fenderi (Family: Lycaenidae). Larvae of this species form facultative mutualisms with ants, who chase away potential predators of the larvae. We used pitfall trapping to compare ant communities between control and herbicide-treated plots through time. The extent to which major changes in plant community composition affect the mutualistic ant community differed among years. Our findings may have relevance for management decisions if the focus of the conservation effort has strong ecological interactions with greatly affected non-target species

A high performance k-NN approach using binary neural networks

This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations. (C) 2003 Elsevier Ltd. All rights reserved

Improved AURA k-Nearest Neighbour approach

The k-Nearest Neighbour (kNN) approach is a widely-used technique for pattern classification. Ranked distance measurements to a known sample set determine the classification of unknown samples. Though effective, kNN, like most classification methods does not scale well with increased sample size. This is due to their being a relationship between the unknown query and every other sample in the data space. In order to make this operation scalable, we apply AURA to the kNN problem. AURA is a highly-scalable associative-memory based binary neural-network intended for high-speed approximate search and match operations on large unstructured datasets. Previous work has seen AURA methods applied to this problem as a scalable, but approximate kNN classifier. This paper continues this work by using AURA in conjunction with kernel-based input vectors, in order to create a fast scalable kNN classifier, whilst improving recall accuracy to levels similar to standard kNN implementations

An Unusual Case of Bit Shyness

In the winter issue of The Veterinary Student (Vol. IX, No.2) a clinical case of non-productive alveolar periostitis was described involving the fourth, upper cheek tooth of a 3-year-old horse. The writer of that case report emphasized the fact that the symptoms produced by such a pathological process vary considerably in each individual case. The following case report is presented to further emphasize this fact and to illustrate the steps that may be taken in order to attempt to alleviate such a pathological condition in a highly valuable animal