Brian Rea

Supporting Systematic Reviews Using Text Mining

In this article, we describe how we are using text mining solutions to enhance the production of systematic reviews. The aims of this collaborative project are the development of a text mining framework to support systematic reviews and the provision of a service exemplar serving as a test bed for deriving requirements for the development of more generally applicable text mining tools and services.

Kleio: a knowledge-enriched information retrieval system for biology

Kleio is an advanced information retrieval (IR) system developed at the UK National Centre for Text Mining (NaCTeM)1. The system offers textual and metadata searches across MEDLINE and provides enhanced searching functionality by leveraging terminology management technologies.

Multi-topic Aspects in Clinical Text Classification

This paper investigates multi-topic aspects in automatic classification of clinical free text. In many practical situ- ations, we need to deal with documents overlapping with multiple topics. Automatic assignment of multiple ICD-9- CM codes to clinical free text in medical records is a typi- cal multi-topic text classification problem. In this paper, we facilitate two different views on multi-topics. The Closed Topic Assumption (CTA) regards an absence of topics for a document as an explicit declaration that this document does not belong to those absent topics. In contrast, the Open Topic Assumption (OTA) considers the missing topics as neutral topics. This paper compares performances of vari- ous interpretations of a multi-topic Text Classification prob- lem into a Machine Learning problem. Experimental results show that the characteristics of multi-topic assignments in the Medical NLP Challenge data is OTA-oriented.

Clinical text classification under the Open and Closed Topic Assumptions

This paper investigates multi-topic aspects in automatic classification of clinical free text in comparison with general text. In this paper, we facilitate two different views on multi-topics: the Closed Topic Assumption (CTA) and the Open Topic Assumption (OTA). Experimental results show that the characteristics of multi-topic assignments in the Computational Medicine Centre (CMC) Medical NLP Challenge Data is strongly OTA-oriented but general text Reuters-21578 is characterised in the middle of the OTA and CTA spectrum.

Improving full text search with text mining tools

Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.

Automated collection strength analysis

The strengths within six library collections were automatically determined through automated enrichment and analysis of bibliographic level metadata records, with a view towards efficient resource sharing and collaborative collection management. This involved very large scale deduplicantion, enrichment and automatic reclassification of records using machine learning processes.