# High Performance Text Mining for Translator

> **NIH NIH OT2** · UNIVERSITY OF COLORADO DENVER · 2021 · $471,239

## 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 organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** LAWRENCE E HUNTER
- **Activity code:** OT2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $471,239
- **Award type:** 3
- **Project period:** 2020-01-23 → 2022-01-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10334356

## Citation

> US National Institutes of Health, RePORTER application 10334356, High Performance Text Mining for Translator (3OT2TR003422-01S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10334356. Licensed CC0.

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