Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data
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Abstract (arXiv)
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.
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References · 7
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
- 2015VQA: Visual Question Answeringcited by 2,506
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- 2020Deep Learning for Financial Applications : A Surveycited by 91
- 2016Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Losscited by 30
- 2020Forecasting financial markets with semantic network analysis in the COVID-19 crisiscited by 5
- 2021Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integrationcited by 1
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