unknown
oaioai:oro.open.ac.uk:23365

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

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 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

    Similar works

    Full text

    thumbnail-image
    Open Research Online

    Open Research Online

    Provided a free PDF
    oaioai:oro.open.ac.uk:23365Last time updated on 5/20/2017View original full text linkProvided by our Sustaining member

    This paper was published in Open Research Online.

    Having an issue?

    Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.