Due to the diversity of natural language processing (NLP) tools and resources, combining
them into processing pipelines is an important issue, and sharing these pipelines with others …

Objective Extracting named entities from clinical free-text presents unique challenges,
particularly when dealing with discontinuous entities—mentions that are separated by …

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to
popular machine learning libraries, a rich feature extraction library that works across …

The clinical narrative contains a great deal of valuable information that is only
understandable in a temporal context. Events, time expressions, and temporal relations …

In recent years there has been an increase in the generation of electronic health records
(EHRs), which lead to an increased scope for research on biomedical literature. Many …

This paper describes a system for span detection and normalization of disorder mentions in
clinical notes as defined in Tasks 1a and 1b of the 2013 ShARe/CLEF eHealth Evaluation …

One of the most difficult and least studied sources of structural ambiguity in text is syntactic
coordination. Coordination resolution, for this dissertation, is the task of determining the …

There is a current trend to combine natural language analysis with research questions from
the humanities. This requires an integration of automatic analysis with manual analysis, eg …

RegulonDB [4] is a database with manually curated knowledge extracted from the literature
describing knowledge of transcriptional regulation in E. coli K-12. It contains objects such as …

We describe an entity detection and resolution system called Newton that is being used to
identify company names in Reuters news articles and ground the mention text to a company …