In a lot of existing legal knowledge based applications, the underlying legal model does not retain isomorphism with the original legal text. Parts of the text have not been modelled, and other parts have been simplified to single if-thenelse clauses. This makes these models difficult to validate, maintain and re-use. In this article we propose to make an intermediate model, in which the origin...

Assistance in retrieving of documents on the World Wide Web is provided either by search engines, through keyword based queries, or by catalogues, which organise documents into hierarchical collections. Maintaining catalogues manually is becoming increasingly difficult due to the sheer amount of material on the Web, and therefore it will be soon necessary to resort to techniques for automatic c...

In order to evaluate image annotation and object categorisation algorithms, ground truth in the form of a set of images correctly annotated with text describing each image is required. Statistics on the WordNet categories of keywords collected from recent automated image annotation and object categorisation publications and evaluation campaigns are presented. These statistics provide a snapshot...

We evaluate the use of Deep Belief Networks as classifiers in a text categorisation task (assigning category labels to documents) in the biomedical domain. Our preliminary results indicate that compared to Support Vector Machines, Deep Belief Networks are superior when a large set of training examples is available, showing an F-score increase of up to 5%. In addition, the training times for DBN...

We describe NRC’s submission to the Anomaly Detection/Text Mining competition organised at the Text Mining Workshop 2007. This submission relies on a straightforward implementation of the probabilistic categoriser described in [4]. This categoriser is adapted to handle multiple labelling and a piecewise-linear confidence estimation layer is added to provide an estimate of the labelling confiden...

This paper reports on the results of a quantitative analysis of user requirements for audiovisual search that allow the categorisation of requirements and to compare requirements across user groups. The categorisation provides clear directions with respect to the prioritisation of system features from the perspective of the development of systems for specific, single user groups and systems tha...

In this paper we report an investigation into the learning of authorship identification or categorisation for the case of e-mail documents. We use various e-mail document features such as structural characteristics and linguistic evidence together with the Support Vector Machine as the learning algorithm. Experiments on a number of e-mail documents give promising results with some e-mail docume...