Siobhan Devlin

Investigating sentence weighting components for automatic summarisation

The work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. It subsequently proved to be a reliable indicator for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component.

Spoken language classification using hybrid classifier combination

In this paper we describe an approach for spoken language analysis for helpdesk call routing using a combination of simple recurrent networks and support vector machines. In particular we examine this approach for its potential in a difficult spoken language classification task based on recorded operator assistance telephone utterances. We explore simple recurrent networks and support vector machines using a large, unique telecommunication corpus of spontaneous spoken language. The main contribution of the paper is a combination of techniques in the domain of call routing. First, we find that simple recurrent networks perform better than support vector machines for this task. Second, we claim that the combination of simple recurrent networks and support vector machines provides slightly improved performance compared to the performance of either simple recurrent networks or support vector machines.

Investigating the Effect of Language and Culture on Student Interaction with and in Webct

Abstract Students studying in Higher Education establishments around the world are increasingly required to interact with Virtual Learning Environments (VLEs) for their education, yet it is not widely understood whether or not factors such as language and culture affect the interaction and even perhaps place some students at a disadvantage to others. This study sought to elicit from students from various educational, cultural and linguistic backgrounds, their experiences and attitudes towards working within this medium. The results showed that language and culture do impact upon a person’s interaction with online materials and the primary research largely supported the work uncovered in the literature survey.