Anaphoric Reference

The task of this project was to annotate a select set of anaphoric relations within a given clinical document and across the paragraphs and sections of that document. The clinical notes were across two institutions and several different report types.

ConText - extended (Temporality) - Long and Short

ConText - original

ConText is based on a negation algorithm called NegEx. ConText's input is a sentence with indexed clinical conditions; ConText's output for each indexed condition is the value for contextual features or modifiers. The initial versions of ConText determines values for three modifiers - Negation: affirmed or negated. Temporality: recent, historical, or hypothetical. Experiencer: patient or other.

A newer version (pyConText) is more extensible and can have user-defined modifiers, One project involving radiology reports added the following modifiers: Uncertainty: certain or uncertain. Quality of radiologic exam: limited or not limited. Severity: critical or non-critical. Sidedness: right or left as well as others.

The google code site contains java and python versions of ConText and NegEx, links to papers describing and evaluating the algorithms, a description of the algorithm (including a list of the trigger terms used for each type of modifier), and a dataset of 120 reports of six types with manually-assigned values to the three modifiers in the original version of ConText. Some ConText trigger terms have translations for Swedish, German, and French.

Indexing Clinical Conditions from ED Reports

This annotation schema describes how to annotate spans of clinical conditions.

Mental Health Twitter Schema

ShARe/CLEF Task 1

ShARe/CLEF Task 2

Tobacco Twitter Schema

Major depressive disorder is one of the most burdensome and debilitating diseases in the United States. We developed a new annotation scheme representing depressive symptoms and psychosocial stressors associated with major depressive disorder and report annotator agreement when applying the scheme to Twitter data.


VA CHIR De-identification