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Turku Event Extraction System

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TEES is available as a stable version (recommended, downloadable from the above icons) or the latest development version. To get started, read the Quick Start guide.

For documentation, see the Wiki and the API docs.



Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments.

Biomedical event extraction refers to the automatic detection of molecular interactions from research articles. Events can accurately represent complex statements, e.g. the sentence “Protein A causes protein B to bind protein C” produces the event CAUSE(A, BIND(B, C)). Such formal structures can be processed with computational methods, allowing large-scale analysis of the literature, as well as applications such as pathway construction.

Example Sentence

TEES has been evaluated in the following Shared Tasks. Models for predicting their targets are provided with TEES and can be used on any unannotated text.

For a list of all scientific publications on TEES, see the Publications page. TEES has been used to successfully predict GENIA type events for all PubMed abstracts and PMC full text articles, and the resulting data is available in the EVEX database.