Temporal fact extraction of fruit cultivation technologies based on deep learning

Xinliang Liu; Lei Ma; Tingyu Mao; Yanzhao Ren; Xinliang Liu; Lei Ma; Tingyu Mao; Yanzhao Ren

doi:10.3934/mbe.2023312

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7217-7233. doi: 10.3934/mbe.2023312

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Research article

Temporal fact extraction of fruit cultivation technologies based on deep learning

1.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2.
School of E-business and Logistics, Beijing Technology and Business University, Beijing 100048, China
3.
National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
4.
School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China

Academic Editor: Vladimir Mityushev

Received: 16 November 2022 Revised: 09 January 2023 Accepted: 22 January 2023 Published: 10 February 2023

There are great differences in fruit planting techniques due to different regional environments. Farmers can't use the same standard in growing fruit. Most of the information about fruit planting comes from the Internet, which is characterized by complexity and heterogeneous multi-source. How to deal with such information to form the convenient facts becomes an urgent problem. Information extraction could automatically extract fruit cultivation facts from unstructured text. Temporal information is especially crucial for fruit cultivation. Extracting temporal facts from the corpus of cultivation technologies for fruit is also vital to several downstream applications in fruit cultivation. However, the framework of ordinary triplets focuses on handling static facts and ignores the temporal information. Therefore, we propose Basic Fact Extraction and Multi-layer CRFs (BFE-MCRFs), an end-to-end neural network model for the joint extraction of temporal facts. BFE-MCRFs describes temporal knowledge using an improved schema that adds the time dimension. Firstly, the basic facts are extracted from the primary model. Then, multiple temporal relations are added between basic facts and time expressions. Finally, the multi-layer Conditional Random Field are used to detect the objects corresponding to the basic facts under the predefined temporal relationships. Experiments conducted on public and self-constructed datasets show that BFE-MCRFs achieves the best current performance and outperforms the baseline models by a significant margin.
- temporal facts,
- information extraction,
- fruit cultivation technologies,
- deep learning,
- temporal expression
Citation: Xinliang Liu, Lei Ma, Tingyu Mao, Yanzhao Ren. Temporal fact extraction of fruit cultivation technologies based on deep learning[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7217-7233. doi: 10.3934/mbe.2023312

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Abstract

There are great differences in fruit planting techniques due to different regional environments. Farmers can't use the same standard in growing fruit. Most of the information about fruit planting comes from the Internet, which is characterized by complexity and heterogeneous multi-source. How to deal with such information to form the convenient facts becomes an urgent problem. Information extraction could automatically extract fruit cultivation facts from unstructured text. Temporal information is especially crucial for fruit cultivation. Extracting temporal facts from the corpus of cultivation technologies for fruit is also vital to several downstream applications in fruit cultivation. However, the framework of ordinary triplets focuses on handling static facts and ignores the temporal information. Therefore, we propose Basic Fact Extraction and Multi-layer CRFs (BFE-MCRFs), an end-to-end neural network model for the joint extraction of temporal facts. BFE-MCRFs describes temporal knowledge using an improved schema that adds the time dimension. Firstly, the basic facts are extracted from the primary model. Then, multiple temporal relations are added between basic facts and time expressions. Finally, the multi-layer Conditional Random Field are used to detect the objects corresponding to the basic facts under the predefined temporal relationships. Experiments conducted on public and self-constructed datasets show that BFE-MCRFs achieves the best current performance and outperforms the baseline models by a significant margin.

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