Zhou, Deyu and He, Yulan
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We propose a biomedical event extraction system, HVS BioEvent, which employs the hidden vector state (HVS) model for semantic parsing. Biomedical events extraction needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In HVS-BioEvent, we further propose novel machine learning approaches for event trigger word identiﬁcation, and for biomedical events extraction from the HVS parse results. Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP’09 shared task, which is only two points lower than the best performing system by UTurku. Nevertheless, HVSBioEvent outperforms UTurku on the extraction of complex event types. The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it can naturally model embedded structural context in sentences.
|Item Type:||Conference Item|
|Copyright Holders:||2011 Not known|
|Academic Unit/Department:||Knowledge Media Institute|
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||18 Apr 2011 08:54|
|Last Modified:||26 Oct 2012 04:46|
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