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Biomedical events extraction using the hidden vector state model

Zhou, Deyu and He, Yulan (2011). Biomedical events extraction using the hidden vector state model. Artificial Intelligence in Medicine, 53(3) pp. 205–213.

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DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1016/j.artmed.2011.08.002
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Abstract

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein–protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extrac-tion is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results.

Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP’09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP’09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku’s system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events.

Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Item Type: Journal Article
Copyright Holders: 2011 Elsevier B.V.
ISSN: 0933-3657
Keywords: abstract annotation; hidden vector state model; semantic parsing; biomedical events extraction
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 29625
Depositing User: Kay Dave
Date Deposited: 30 Sep 2011 09:52
Last Modified: 24 Oct 2012 10:04
URI: http://oro.open.ac.uk/id/eprint/29625
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