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.

精准农业应用领域中，使用传感器和数据分析技术检测和测量作物健康威胁是一个重要的应用场景。然而，由于缺乏标注数据和细粒度的语义资源，文本数据在现有解决方案中仍未得到充分探索。最近的研究表明，农民的日益连接性和在线农业社区的出现使得类似 Twitter 的社交媒体成为检测陌生植物健康事件的参与平台，只要我们能从非结构化文本数据中提取关键信息。ChouBERT 是法语预训练语言模型，能够准确识别涉及观察植物健康问题的推文，并且能推广到未见过的自然灾害。本文通过进一步研究 ChouBERT 在小规模标记数据上进行的令牌级注释任务，解决了标注数据的不足问题。

基于 ChouBERT 方法的推文中植物健康威胁的命名实体识别

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

The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train.

本文从信号处理角度提供了关于高阶张量分解的全面介绍，包括基本的 CP 和 Tucker 模型，通过多种数据处理方法提供更加灵活和自然的潜在因素；研究表明张量分解可用作现代信号处理、数据分析和机器学习应用的最有效和最有前途的工具。