论文标题
智能农业：一种针对非结构化数据进行农业风险评估的新型多层次方法
Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data
论文作者
论文摘要
对于大多数人来说，检测大量文本数据的机会和威胁是一项具有挑战性的任务。传统上，公司将主要依靠结构化数据来检测和预测风险，而失去了可以从非结构化文本数据中提取的大量信息。幸运的是，人工智能通过在数据提取和处理技术中进行创新来解决这个问题，从而使我们能够理解和利用自然语言数据，并将其转变为机器可以处理并从中提取洞察力的结构。不确定性是指不知道将来会发生什么的状态。本文旨在利用自然语言处理和机器学习技术来建模不确定性，并使用大量文本数据评估每个不确定性集群中的风险水平。
Detecting opportunities and threats from massive text data is a challenging task for most. Traditionally, companies would rely mainly on structured data to detect and predict risks, losing a huge amount of information that could be extracted from unstructured text data. Fortunately, artificial intelligence came to remedy this issue by innovating in data extraction and processing techniques, allowing us to understand and make use of Natural Language data and turning it into structures that a machine can process and extract insight from. Uncertainty refers to a state of not knowing what will happen in the future. This paper aims to leverage natural language processing and machine learning techniques to model uncertainties and evaluate the risk level in each uncertainty cluster using massive text data.
