Robert Munro

• Edinburgh Napier University

We describe the process of mining complex relationships in spatial databases using the maximal participation index (maxPI), which has a property of discovering low support and high confidence rules. Complex relationships are defined as those involving two or more of: multifeature co-location, self-co-location, one-to many relationships, self-exclus...

This paper presents a named entity classifica-tion system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine at-tribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results.

This paper describes a novel model for term frequency distributions that is derived from queuing-theory. It is compared with Poisson distributions, in terms of how well the models describe the observed distributions of terms, and it is demonstrated that a model for term frequency distributions based on queue utilisation generally gives a better fit...

This paper describes the need for mining complex relationships in spatial data. Complex relationships are defined as those involving two or more of: multi-feature co-location, self-co-location, one-to-many relationships, self-exclusion and multi-feature exclusion. We demonstrate that even in the mining of simple relationships, knowledge of complex...

The task of creating a lexical knowledgebase has been defined in our work as extracting appropriate semantic phenomena from what are essentially print dictionaries stored in a desktop publishing format. The aim of this work is to achieve automatic identification of structural elements in the dictionaryy's stream of text that are isomorphic to seman...

The Sydney Language Independent Named Entity Recogniser and Classi er (SLINERC) is a multi-stage system for the recognition and classi cation of named entities. Each stage uses a decision graph learner to combine statistical features with results from prior stages. Earlier stages are focused upon entity recognition, the division of non-entity terms...

The task of creating a lexical knowledgebase has been defined in our work as extracting appropriate se- mantic phenomena from what are es- sentially print dictionaries stored in a desktop publishing format. The aim of this work is to achieve automatic iden- tification of structural elements in the dictionaryy's stream of text that are iso- morphic...

The notion of language as probabilistic is well known within Systemic Functional Linguistics. Aspects of language are discussed as meaningful tendencies, not as deterministic rules. In past computational representations of functional grammars, this probabilistic property has typically been omitted. This paper will present the results of a recent pr...

The biggest shortcoming of efficient Bayesian learning is the attribute independence assumption. Current state-of-the-art learners weaken this to capture pairwise correla-tions with quadratic efficiency. This paper details a recent advance in mining correlated data through the integration of techniques from the fields of outlier detection, clusteri...

This project presents an application tool, FERRET III, which allows users to identify structure in a stream of text. The structure can then be applied to other text streams in the same docu-ment or other documents. Experimen-tal work demonstrates the effective-ness of the system for priming an au-tomaton with rules for text segmenta-tion from a few...