School of CS newsletterPublished: Monday, 13 July 2015
Weekly newsletter for the School of CS
[ top ]News from Head of School
Thank you to everyone who donated to Brainstrust in return for cake at the BBQ on Friday. We raised a fantastic £230 which has been sent to the charity today.
[ top ]Events
14 July, 14:00, Atlas 1, Kilburn building.
Speaker: Irena Spasić, University of Cardiff
Abstract: In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this talk we describe KneeTex, an information extraction system that operates in this domain.
As an ontology–driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis.
In this study we demonstrated how automatic term recognition can facilitate the development of a domain–specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico–semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co–reference resolution, followed by text segmentation. Ontology–based semantic typing is then used to drive the template filling process.
KneeTex extracts information with precision of 98.00%, recall of 97.63% and F–measure of 97.81%, the values of which are in line with human–like performance.
Speaker: Irena Spasic is a senior lecturer in the School of Computer Science & Informatics, specialising in Health Informatics, text mining, information management, knowledge representation and machine learning.
20 July, 10:30, Lecture Theatre 1.4, Kilburn building.
Speaker: Dr Peter Hsu. Oracle Labs
Host: Mikel Lujan