Zhou, Deyu and He, Yulan
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1016/j.artmed.2011.08.002|
|Google Scholar:||Look up in Google Scholar|
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.
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.
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.|
|Keywords:||abstract annotation; hidden vector state model; semantic parsing; biomedical events extraction|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||30 Sep 2011 09:52|
|Last Modified:||17 Aug 2016 18:46|
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