Ontology-Based Protein-Protein Interactions Extraction from Literature using the Hidden Vector State Model
BibTeX
@MISC{He_ontology-basedprotein-protein,
author = {Yulan He and Keiichi Nakata and Deyu Zhou},
title = {Ontology-Based Protein-Protein Interactions Extraction from Literature using the Hidden Vector State Model},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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 un-annotated 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. 1
Keyphrases
hidden vector state model ontology-based protein-protein interaction extraction hvs model biomedical literature hidden vector state protein-protein interaction information extraction un-annotated data ppi extraction ppi ontology undirected graphical model constrained way whilst seamless fusion relation extraction hvs model belongs background knowledge statistical learning new paradigm entity relationship non-local information novel framework ontology inference ontology knowledge non-local feature deep natural language understanding
