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Trigner is an open source machine learning-based solution for biomedical event trigger recognition. It takes advantage of Conditional Random Fields (CRFs) with a high-end feature set, including linguistic-based, orthographic, morphological, local context and dependency parsing features. Additionally, a completely configurable algorithm is used to automatically optimize the feature set and training parameters for each event type, selecting the features that have a positive contribution and optimizing the CRF model order, n-grams sizes, vertex information and maximum hops for dependency parsing features. The final output consists of various CRF models, each one optimized to the linguistic characteristics of each event type.



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Download resources ( GDep, Corpora, Models and Dictionaries )


The following utility scripts are provided:

  • perform sentence splitting, tokenization, lemmatization, POS tagging, chunking and dependency parsing on input data in A1 format and store the resulting output in a compressed file;
  • train a model to recognize a specific or a set of event triggers;
  • find the optimal model configuration of a specific event trigger;
  • annotate a set of documents using a set of models and/or dictionaries;
  • evalute the quality of the provided event triggers, which are provided in A1 format;
  • generate dictionaries of event triggers based on input data in A1 format;
  • randomly split a corpus into two parts, following a provided ratio;
  • combine multiple corpora files into a single corpus file.

Please use the option "-h" for further help on each script.

Bug tracker

Have a bug? Please create an issue here on GitHub!


David Campos: david.campos(a)

Sérgio Matos: aleixomatos(a)

José Luís Oliveira: jlo(a)


Copyright (C) 2013 David Campos, Universidade de Aveiro, Instituto de Engenharia Electrónica e Telemática de Aveiro

Trigner is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit


Trigger recognition for event mining.



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