Investigating annotation noise for named entity recognition

Author:

Zhu YuORCID,Ye Yingchun,Li Mengyang,Zhang Ji,Wu Ou

Funder

National Nature Science Foundation of China

Natural Science Foundation of Tianjin City

Zhijiang Fund

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference48 articles.

1. Aguilar G, López-Monroy AP, González FA, et al (2019) Modeling noisiness to recognize named entities using multitask neural networks on social media. Preprint at arXiv:1906.04129

2. Akbik A, Blythe D, Vollgraf R (2018) Contextual string embeddings for sequence labeling. In: Proceedings of the 27th international conference on computational linguistics, pp 1638–1649

3. Akbik A, Bergmann T, Blythe D, et al (2019) FLAIR: An easy-to-use framework for state-of-the-art NLP. In: Proceedings of the 2019 annual conference of the North American chapter of the association for computational Linguistics, pp 54–59

4. Apratim B. BSMario . (2018) Long-term on-board prediction of people in traffic scenes under uncertainty. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4194–4202

5. Derczynski L, Nichols E, van Erp M, et al (2017) Results of the WNUT2017 shared task on novel and emerging entity recognition. In: Proceedings of the 3rd workshop on noisy user-generated text, pp 140–147

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