RiTUAL-MBZUAI/style_NER
“Data Augmentation for Cross-Domain Named Entity Recognition” (EMNLP 2021)
This project helps developers improve Named Entity Recognition (NER) models for specialized text by generating more training data. It takes well-annotated text from a general domain and transforms its style and characteristics to resemble text from a specific, data-scarce domain. The output is augmented training data that helps train more accurate NER models for niche applications.
No commits in the last 6 months.
Use this if you are a developer struggling to build accurate Named Entity Recognition (NER) models for specialized text because you lack sufficient labeled training data in that domain.
Not ideal if you are looking for an off-the-shelf NER solution or if you do not have access to an existing high-resource dataset to augment.
Stars
20
Forks
4
Language
Python
License
MIT
Category
Last pushed
Apr 04, 2022
Commits (30d)
0
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