Check the preview of 2nd version of this platform
being developed by the open MLCommons taskforce on automation and reproducibility
as a free, open-source and technology-agnostic on-prem platform.
Government Domain Named Entity Recognition for South African Languages
lib:c111bb5f3fd44a57 (v1.0.0)
Authors: Roald Eiselen
Where published: LREC 2016 5
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/L16-1533/
This paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data sets with at least 15,000 annotated named entity tokens for ten of the official South African languages. The description of the protocols and annotated data sets provide an overview of the problems encountered during the annotation of the data sets. Based on these annotated data sets, CRF named entity recognition systems are developed that leverage existing linguistic resources. The newly created named entity recognisers are evaluated, with F-scores of between 0.64 and 0.77, and error analysis is performed to identify possible avenues for improving the quality of the systems.
Where published: LREC 2016 5
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/L16-1533/
This paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data sets with at least 15,000 annotated named entity tokens for ten of the official South African languages. The description of the protocols and annotated data sets provide an overview of the problems encountered during the annotation of the data sets. Based on these annotated data sets, CRF named entity recognition systems are developed that leverage existing linguistic resources. The newly created named entity recognisers are evaluated, with F-scores of between 0.64 and 0.77, and error analysis is performed to identify possible avenues for improving the quality of the systems.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)