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Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

He, Yulan; Nakata, Keiichi and Zhou, Deyu (2008). Ontology-based protein-protein interactions extraction from literature using the hidden vector state model. In: Workshop on Semantic Aspects in Data Mining at The IEEE International Conference on Data Mining series (ICDM 2008), 15-19 Dec 2008, Pisa, Italy.

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This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the hidden vector state (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from unannotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

Item Type: Conference Item
Copyright Holders: 2008, IEEE
Extra Information: IEEE International Conference on Data Mining Workshops, 2008.
ICDMW '08. Pisa, 15-19 Dec. 2008

ISBN: 978-0-7695-3503-6

Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
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Item ID: 23365
Depositing User: Kay Dave
Date Deposited: 18 Nov 2010 10:14
Last Modified: 01 Mar 2016 05:00
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