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arXiv · 2209.15108

How to tackle an emerging topic? Combining strong and weak labels for Covid news NER

Aleksander Ficek·Fangyu Liu·Nigel Collier
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Abstract

Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and datasets. For a realistic evaluation setup, we introduce a novel COVID-19 news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotated strongly labelled sentences and 13000 auto-generated weakly labelled sentences. Besides the dataset, we propose CONTROSTER, a recipe to strategically combine weak and strong labels in improving NER in an emerging topic through transfer learning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER while providing analysis on combining weak and strong labels for training. Our key findings are: (1) Using weak data to formulate an initial backbone before tuning on strong data outperforms methods trained on only strong or weak data. (2) A combination of out-of-domain and in-domain weak label training is crucial and can overcome saturation when being training on weak labels from a single source.

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Aleksander Ficek, Fangyu Liu, Nigel Collier. 2022-09-29. How to tackle an emerging topic? Combining strong and weak labels for Covid news NER. https://arxiv.org/abs/2209.15108

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