- Oral presentation
- Open Access
Event extraction on PubMed scale
© Ginter et al; licensee BioMed Central Ltd. 2010
- Published: 06 October 2010
- Unique Event
- Share Task
- Event Extraction
- Syntactic Analyze
There has been a growing interest in typed, recursively nested events as the target for information extraction in the biomedical domain. The BioNLP'09 Shared Task on Event Extraction  provided a standard definition of events and established the current state-of-the-art in event extraction through competitive evaluation on a standard dataset derived from the GENIA event corpus.
We have previously established the scalability of event extraction to large corpora  and here we present a follow-up study in which event extraction is performed from the titles and abstracts of all 17.8M citations in the 2009 release of PubMed. The extraction pipeline is composed of state-of-the-art methods: the BANNER named entity recognizer , the McClosky-Charniak domain-adapted parser , and the Turku Event Extraction System , the winning entry of the Shared Task.
The resulting dataset consists of over 19.2M instances of 4.5M unique events, of which 2.1M instances of 1.6M unique events recursively involve at least two different named entities. This dataset is several orders of magnitude larger than any previous event extraction effort and -- having been obtained by a demonstrably state-of-the-art pipeline — represents the most accurate event extraction output achievable with presently available tools. Compiling the dataset was a technically challenging undertaking and required roughly 8,300 CPU-hours.
As the primary contribution of the study, we make the entire set of extracted events freely available at http://bionlp.utu.fi, together with the output of the individual stages of the pipeline, such as 36.5M named entity instances and syntactic analyzes for all 20M sentences containing at least one named entity. This resource will facilitate future research related to biological event networks by providing a standard, publicly available, large-scale dataset, avoiding the unnecessary duplication of efforts in executing the complex event extraction pipeline.
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This article is published under license to BioMed Central Ltd.