Text content and task performance in the evaluation of a natural language generation system

An important question in the evaluation of Natural Language Generation systems concerns the relationship between textual characteristics and task performance. If the results of task-based evaluation can be correlated to properties of the text, there are better prospects for improving the system. The present paper investigates this relationship by focusing on the outcomes of a task-based evaluation of a system that generates summaries of patient data, attempting to correlate these with the results of an analysis of the system’s texts, compared to a set of gold standard human-authored summaries.peer-reviewe

Evaluating algorithms for the generation of referring expressions : going beyond toy domains

We describe a corpus-based evaluation methodology, applied to a number of classic algorithms in the generation of referring expressions. Following up on earlier work involving very simple domains, this paper deals with the issues associated with domains that contain ‘real-life’ objects of some complexity. Results indicate that state of the art algorithms perform very differently when applied to a complex domain. Moreover, if a version of the Incremental Algorithm is used then it becomes of huge importance to select a good preference order. These results should contribute to a growing debate on the evaluation of nlg systems, arguing in favour of carefully constructed balanced and semantically transparent corpora.peer-reviewe

Recent Advances in Dermatology Volume 2

xiv+370hlm.;24c

Bioinformatics And Brain Imaging:

This chapter reviews some exciting new techniques for analyzing brain imaging data. We describe computer algorithms that can discover patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, and normal and abnormal brain development, based on imaging data collected in large human populations. Extraordinary patterns can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, and how it changes in disease, or responds to medication. Genetic brain maps reveal which aspects of brain structure are inherited, shedding light on the nature/nurture debate. They also identify deficit patterns in those at genetic risk for disease. Probabilistic brain atlases now store thousands of these brain maps, models, and images, collected with an array of imaging devices (MRI/fMRI, PET, 3D cryosection imaging, histology). These atlases capture how the brain varies with age, gender, demographics, and in disease. They relate these variations to cognitive, therapeutic, and genetic parameters. With the appropriate computational tools, these atlases can be stratified to create average maps of brain structure in different diseases, revealing unsuspected features. We describe the tools to interact with these atlases. We also review some of the technical and conceptual challenges in comparing brain data across large populations, highlighting some key neuroscience applications