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Computer Science > Computation and Language

arXiv:2310.12522 (cs)
[Submitted on 19 Oct 2023]

Title:Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach

Authors:Shufan Jiang (CRESTIC, ISEP), Rafael Angarita (ISEP), Stéphane Cormier (CRESTIC), Francis Rousseaux (CRESTIC)
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Abstract:An important application scenario of precision agriculture is detecting and measuring crop health threats using sensors and data analysis techniques. However, the textual data are still under-explored among the existing solutions due to the lack of labelled data and fine-grained semantic resources. Recent research suggests that the increasing connectivity of farmers and the emergence of online farming communities make social media like Twitter a participatory platform for detecting unfamiliar plant health events if we can extract essential information from unstructured textual data. ChouBERT is a French pre-trained language model that can identify Tweets concerning observations of plant health issues with generalizability on unseen natural hazards. This paper tackles the lack of labelled data by further studying ChouBERT's know-how on token-level annotation tasks over small labeled sets.
Comments: 2022 6th International Conference on Universal Village (UV), Oct 2022, Boston, United States
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2310.12522 [cs.CL]
  (or arXiv:2310.12522v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2310.12522
Related DOI: https://doi.org/10.1109/UV56588.2022.10185492

Submission history

From: Shufan JIANG [view email] [via CCSD proxy]
[v1] Thu, 19 Oct 2023 06:54:55 UTC (709 KB)
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