High Performance Text Mining for Translator

NIH RePORTER · NIH · OT2 · $471,239 · view on reporter.nih.gov ↗

Abstract

We propose to build a knowledge provider that will seek out, integrate and provide AIready, BioLink-compatible models via high-performance text-mining of the biomedical literature. Problems with Translator’s current mining of the biomedical literature that we intend to solve include: (1) weaknesses in framework extensibility and benchmarking that make integrating and validating new text-mining approaches difficult; (2) problematic licensing of software, terminologies and other resources that do not adequately support FAIR (and TLC) best practices; (3) processing only PubMed titles and abstracts, not full text publications; (4) Translator’s use of older NLP technology with relatively poor performance; (5) lack of a mechanism for community feedback regarding errors and other problems; (6) lack of continuous updates to add knowledge from new publications; (7) output knowledge representation that is simplistic and vague, failing to reflect the richness of what is expressed in scientific documents.

Key facts

NIH application ID
10334356
Project number
3OT2TR003422-01S1
Recipient
UNIVERSITY OF COLORADO DENVER
Principal Investigator
LAWRENCE E HUNTER
Activity code
OT2
Funding institute
NIH
Fiscal year
2021
Award amount
$471,239
Award type
3
Project period
2020-01-23 → 2022-01-22