📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Getting more out of biomedical documents with GATE's full lifecycle open source text analytics. (2013)

First Author: Cunningham H

Attributed to: Machine Learning Methods for Personalised, Abstractive Summarisation of Consumer-Generated Media funded by EPSRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1371/journal.pcbi.1002854

PubMed Identifier: 23408875

Publication URI: http://europepmc.org/abstract/MED/23408875

Type: Journal Article/Review

Volume: 9

Parent Publication: PLoS computational biology

Issue: 2

ISSN: 1553-734X