MLNet:一个多层次的多式联运命名实体识别架构
Hanming Zhai1, Xiaojun Lv2, Zhiwen Hou1
1School of Information Network Security, People's Public Security University of China, Beijing, China.
Frontiers in neurorobotics
|July 6, 2023
概括
本研究引入了一种新的多式联运命名实体识别 (MNER) 架构,以提高对象识别精度. 该模型通过过视觉信息和减少文本噪音来增强语义理解,以改善机器人交互.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 准确识别说话的物体对于机器人的决策和建议至关重要.
- 命名实体识别 (NER) 和对象检测 (OD) 是NLP和CV中的对象识别的基础.
- 现有的多式联网方法显示出希望,但需要优化在多式联网命名实体识别 (MNER) 中对杂,短文本图像数据进行优化.
研究的目的:
- 提出一个新的多级多式联运命名实体识别架构.
- 在MNER任务中增强语义理解和实体识别有效性.
- 解决当前基于图像-文本的MNER架构的局限性,特别是在噪音数据方面.
主要方法:
- 独立的图像和文本编码.
- 一个对称的基于变压器的神经网络,用于多模式功能融合.
- 一个封闭机制来过相关的视觉信息并增强文本理解.
- 字符级向量编码以减轻文本噪音.
- 条件随机字段用于标签分类.
主要成果:
- 拟议的模型在Twitter数据集上的MNER任务中显示出更高的准确性.
- 视觉信息的有效过改善了语义歧义.
- 字符级编码减少了文本噪声对识别准确性的影响.
结论:
- 新的多级多式联运架构显著提高了MNER任务性能.
- 网关机制和字符级编码是处理杂多式联运数据的有效策略.
- 这项工作通过改进对象识别来提高机器人的理解和与环境互动的能力.
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