# Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data

arXiv: https://arxiv.org/abs/2211.12515 · HTML: https://mlsanity.com/paper/2211.12515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://mlsanity.com/paper/1604.05529/) (2016)
- [Forecasting financial markets with semantic network analysis in the COVID-19 crisis](https://mlsanity.com/paper/2009.04975/) (2020)
- [Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration](https://mlsanity.com/paper/2108.04343/) (2021)

## 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.