Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage Modellingnamed-entity-recognitionNamed Entity RecognitionSimilar Papers 제목 키워드 기반
Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition
This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results. Among ten registered teams, se…
Chinese Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Experiments to Improve Named Entity Recognition on Turkish Tweets
Social media texts are significant information sources for several application areas including trend analysis, event monitoring, and opinion mining. Unfortunately, existing solutions for tasks such as named entity recogn…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Opinion MiningGNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, rout…
Entity Extraction using GANnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2Gated Graph Sequence Neural Networks for Chinese Healthcare Named Entity Recognition
Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage
Artificial intelligence has significantly advanced the automation of diagnostic processes, benefiting various fields including agriculture. This study introduces an AI-based system for the automatic diagnosis of urban st…
Computational EfficiencyDiagnosticHybrid Machine LearningImage Segmentation+1