Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach
This work provides a domain-specific solution for precision agriculture by enabling better detection of plant health events from social media data, though it is incremental as it builds on an existing model.
The paper addresses the lack of labeled data for monitoring plant health threats in tweets by applying ChouBERT, a French pre-trained language model, to token-level annotation tasks on small labeled sets, achieving generalizability on unseen natural hazards.
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.