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

Autor:	Jiang, Shufan, Angarita, Rafael, Cormier, Stéphane, Rousseaux, Francis
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/UV56588.2022.10185492
Popis:	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. Comment: 2022 6th International Conference on Universal Village (UV), Oct 2022, Boston, United States
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.12522 Zobrazit plný text záznamu View this record from Arxiv