Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics
- Submitting institution
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The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40100273
- Type
- D - Journal article
- DOI
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10.1186/1758-2946-7-S1-S6
- Title of journal
- Journal of Cheminformatics
- Article number
- S6
- First page
- -
- Volume
- 7
- Issue
- (Suppl 1): S6
- ISSN
- 1758-2946
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
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A - Computer Science
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Novel methods implemented in a software system (ChER).
Competed in BioCreative IV (an international community-wide effort for evaluating biomedical text-mining systems):
- Won 1st place in the Chemical Name Indexing task (out of 23 international participants)
- Won 3rd place in the Named Entity Recognition task (out of 26 international participants).
Invited talk in an open science conference (OpenAIRE-COAR 2014, Greece).
Enabled follow-on funding for text mining workflows in analysing chemical literature:
- BBSRC EMPATHY (BB/M006891/1, GBP594,000)
- Japan Partnering Award (BB/P025684/1, GBP39,800)"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
