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To get started with TEES, download the latest stable release or the current version from the repository. After downloading, TEES can optionally be installed as a module using "setup.py", but this is not required, and the program can simply be used from the unpacked archive.
However, before using TEES the external programs and datafiles need to be installed using the interactive configuration tool configure.py, located in the package root directory.
After TEES had been configured, you can predict events or relations for text with classify.py. Using the "-m" (model) switch, you can select one of the pre-computed models (listed here). For example, to run TEES prediction for the BioNLP 2011 GENIA development corpus, use:
python classify.py -m GE11-devel -i GE11-devel -o [OUTSTEM]
where "OUTSTEM" is the output file stem. To try TEES on unannotated text, you can give "classify.py" a PubMed citation id, such as:
python classify.py -m GE11 -i 9668063 -o [OUTSTEM]
TEES will download the abstract and use the integrated preprocessing pipeline to split the text into sentences (with the GENIA Sentence Splitter), detect named entities (with BANNER) and parse the text (with BLLIP Parser using David McClosky's biomodel and Stanford format conversion), after which events are detected from the document.
The primary user interface to TEES consists of the following programs
- classify.py - Predict events/relations with an existing model
- train.py - Train a new event/relation extraction model
- batch.py - Batch process large sets of input files
- configure.py - Install TEES models, external tools and corpora
For information on using these programs, see the TEES wiki at https://github.com/jbjorne/TEES/wiki
TEES also has a number of modules that can be used as standalone executables, including the wrappers for external tools such as parsers. A list of these executables can be found at https://github.com/jbjorne/TEES/wiki/Programs.