Biomedical events extraction using the hidden vector state model

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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 extraction 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.

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  • Biomedical events extraction

    Rights statement: NOTICE: this is the author’s version of a work that was accepted for publication in Artificial intelligence in medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Zhou, D & He, Y, 'Biomedical events extraction using the hidden vector state model' Artificial intelligence in medicine, vol.53, no. 3 (2011) DOI tp://dx.doi.org/10.1016/j.artmed.2011.08.002.

    Accepted author manuscript, 227 KB, PDF-document

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Original languageEnglish
Pages (from-to)205-213
Number of pages9
JournalArtificial Intelligence in Medicine
Volume53
Issue3
DOIs
StatePublished - Nov 2011

Bibliographic note

NOTICE: this is the author’s version of a work that was accepted for publication in Artificial intelligence in medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Zhou, D & He, Y, 'Biomedical events extraction using the hidden vector state model' Artificial intelligence in medicine, vol.53, no. 3 (2011) DOI tp://dx.doi.org/10.1016/j.artmed.2011.08.002.

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