Neil Barrett

Neil Barrett
research category image

Main area of research
Natural language & AI

Neil Barrett is a Senior NLP Research Engineer in the Clinical Language Understanding group with research interests in the areas of semantic reasoning, automated clinical coding, information retrieval and machine learning. Prior to joining Nuance, Neil was a PhD student at the University of Victoria, Canada. His current areas of research are automated clinical coding and the application of unsupervised learning to clinical language understanding. He has B.S., M.Sc. and Ph.D. in computer science from Mcgill University, Memorial University of Newfoundland and the University of Victoria, respectively. His masters in human-computer interaction focused on embodied conversational agents and his doctorate focused on engineering a novel clinical language understanding system. Between his undergraduate degree and graduate degrees, Neil worked as a programmer and consultant on various contracts ranging from flight simulation tools to Montreal exchange options trading.

Selected articles

A Wizard-of-Oz platform for embodied conversational agents

A low-cost prototyping environment for experimenting with embodied conversational agents is discussed. The platform allows modeling and experimenting with different agent constructs and protocols prior

Read more

Applying natural language processing toolkits to electronic health records — An experience report

A natural language challenge devised by Informatics for Integrating Biology and the Bedside (i2b2) was to analyze free-text health data to construct a multi-class, multi-label

Read more

eHealth interoperability with web-based medical terminology services – a study of service requirements and maturity

Interoperability of health information systems requires common clinical terminologies and services that make those terminologies available on a shared infrastructure, i.e. Web-based terminology servers. Terminology

Read more

Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm (2010)

Proper tokenization of biomedical text is a nonĀ­trivial problem. Problematic characteristics of current biomedical tokenizers include idiosyncratic tokenizer output and poor tok-enizer extensibility and reuse.

Read more

Building a biomedical tokenizer using the token lattice design pattern and the adapted viterbi algorithm (2011)

Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts. Other than rule

Read more


1 2