Fundamental Teaching Department, Huanghe Jiaotong University, Jiaozuo 454950, China.
Comput Intell Neurosci. 2022 Apr 28;2022:6344571. doi: 10.1155/2022/6344571. eCollection 2022.
Feature extraction and Chinese translation of Internet-of-Things English terms are the basis of many natural language processing. Its main purpose is to extract rich semantic information from unstructured texts to allow computers to further calculate and process them to meet different types of NLP-based tasks. However, most of the current methods use simple neural network models to count the word frequency or probability of words in the text, and it is difficult to accurately understand and translate IoT English terms. In response to this problem, this study proposes a neural network for feature extraction and Chinese translation of IoT English terms based on LSTM, which can not only correctly extract and translate IoT English vocabulary but also realize the feature correspondence between English and Chinese. The neural network proposed in this study has been tested and trained on multiple datasets, and it basically fulfills the requirements of feature translation and Chinese translation of Internet-of-Things terms in English and has great potential in the follow-up work.
物联网英文术语的特征提取和中文翻译是许多自然语言处理的基础。它的主要目的是从非结构化文本中提取丰富的语义信息,使计算机能够进一步计算和处理这些信息,以满足不同类型的基于自然语言处理的任务。然而,目前大多数方法都使用简单的神经网络模型来计算文本中单词的频率或概率,很难准确理解和翻译物联网英文术语。针对这一问题,本研究提出了一种基于 LSTM 的物联网英文术语特征提取和中文翻译的神经网络,它不仅可以正确提取和翻译物联网英文词汇,还可以实现英文和中文之间的特征对应。本研究提出的神经网络已经在多个数据集上进行了测试和训练,它基本满足了物联网英文术语的特征翻译和中文翻译的要求,在后续工作中有很大的潜力。