Paul Thompson | 2 May 15:55 2012

New paper on improving event extraction using coreference resolution and domain adaptation

Boosting automatic event extraction from the literature using domain adaptation and coreference resolution

Makoto Miwa, Paul Thompson and Sophia Ananiadou 

Bioinformatics 2012; doi: 10.1093/bioinformatics/bts237
In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems’ ability to recognise biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage.

We have enhanced our existing EE system, EventMine, in two ways. Firstly, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP’11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP’09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP’11 shared task.

The improved version of EventMine, incorporating the CR system and DA methods, is available at:


Paul Thompson
Research Associate
School of Computer Science
National Centre for Text Mining
Manchester Interdisciplinary Biocentre
University of Manchester
131 Princess Street
M1 7DN
Tel: 0161 306 3091

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