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Deep Neural Networks for Image-Based Dietary Assessment
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食品安全新闻事件通过层次变压器模型进行分类
Shufeng Xiong1, Wenjie Tian1, Vishwash Batra2
1Henan Agricultural University, Zhengzhou, 450002, China.
Heliyon
|July 17, 2023
概括
本研究引入了一种新的分层变压器模型来分类冗长的食品安全新闻报道,克服现有模型的局限性. 新方法有效地对信息进行分类,改善食品安全警报和信息披露.
科学领域:
- 食品安全 食品安全
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 越来越多的在线食品安全新闻需要有效的信息提取和分类.
- 现有的文本分析模型在与食品安全报告中典型的长文件格式作斗争.
- 准确的分类对于及时和相关的食品安全警报至关重要.
研究的目的:
- 为分类长篇食品安全新闻文件提出一种新型模型.
- 解决预先训练的语言模型在处理长文本方面的局限性.
- 提高食品安全新闻分类的准确性,以改善警报系统.
主要方法:
- 开发了一个层次化的变压器模型用于长文本分类.
- 输入处理涉及将文档分割成块,并识别必要的句子.
- 使用BERT进行初始文本块分析,然后进行基于Transformer的特征转换.
主要成果:
- 与现有的文本分类方法相比,提出的层次化变压器模型表现出优越的性能.
- 对比实验证实了该模型在食品监管数据集上的有效性.
- 该模型成功地以高准确度对食品安全新闻进行了分类.
结论:
- 层次化变压器模型是分类长期食品安全新闻文件的有效解决方案.
- 这种方法为专门领域的自然语言处理领域带来了进步.
- 这些发现支持改善食品法规中的信息披露和安全警报系统.
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