# Research on Feature Extraction and Chinese Translation Method of Internet-of-Things English Terminology

Authors: Huasu Li
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

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.


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## Body

## 1. Introduction

Feature extraction and Chinese translation of the Internet-of-Things English terms are the basis of most natural language processing [1–5]. Its main task is to extract rich semantic information from unstructured text, which is more convenient for the computer to further calculate and process and meet more follow-up requirements [6–12]. NLP stands for natural language processing and is an important branch of deep learning. Its main function is to extract the required information from the text data file and realize the correspondence between text and semantic information. NLP-based tasks [13–19] are under normal circumstances, text semantic feature extraction provides a solid foundation for text understanding, and Chinese translation of English terms is based on semantic feature understanding. Language conversion and correspondence are carried out on the basis of semantic feature understanding and information design text comprehension methods. As far as the current application scope of NLP is concerned, the feature extraction and Chinese translation methods [20] [22–29] of Internet-of-Things English terms have great potential value.The method based on text feature extraction has a wide range of applications and has different uses for different scenarios. The method in this study is mainly aimed at the method of feature extraction of the English terminology of the Internet of Things, and the object-oriented object is the Internet of Things, which can be said to be a subset of the former. Text semantic feature extraction is the basis for realizing text understanding. The quality of semantic text feature extraction directly affects the accuracy of the text semantic understanding model. Semantic text feature extraction is to extract the key semantic information in the text so that the computer can process natural text data quickly and without ambiguity. Specifically, the relationship among words is extracted by mapping the words in the text to the appropriate semantic feature space. Although there are many ways to solve these problems, there are still serious problems. When the text semantic feature extraction method based on these methods is used for semantic understanding, there are different problems in understanding from different perspectives among words that seem to have a semantic similarity. This is because the text semantic feature extraction method of word bag or word vector is to count the frequency or probability distribution of text words and does not include contextual semantic information between words, and its semantic understanding method cannot solve the problem that words in the text depend on context. With the advent of knowledge graphs and perceptrons, discretized and highly semantically concentrated texts are transformed into semantic representations that machines can understand and compute. Therefore, on the basis of traditional semantic feature extraction, each dimension element in the extracted semantic features has a clear meaning by designing a more effective semantic text feature extraction method. The marked English text corpus is trained by the method of deep learning; the words are mapped to specific knowledge concepts, the semantic features of the words and their concepts in the text are extracted, and the contextual concept dependencies of the words in the text are mined to solve the text semantic feature extraction. This method is used to solve the problem of text semantic feature extraction and sparse word semantic features.Most of the current text semantic feature extraction methods mainly use neural network models to generate text representations [30–45]. Most of these models use the frequency or probability distribution of words in the statistical text to represent English professional vocabulary in the form of semantic space to construct a text semantic representation model. However, these methods have two problems in the feature extraction process of English terminology of the Internet of Things. One is that the common vocabulary and the direction of the Internet of Things use the same vocabulary to express different meanings; that is, the same vocabulary will have ambiguity [46–52]. Second is, generally speaking, English feature extraction and Chinese translation of the Internet of Things are two steps, which are to extract the English terms of the Internet of Things and convert the English terms of the Internet of Things to Chinese [52–58]. Usually, two network models are used to realize this function. The structure of the model is complex, and the actual operation is difficult. To solve this problem, this study proposes a feature extraction and translation network for IoT English terminology based on LSTM, which can basically correctly extract and translate IoT English terminology vocabulary.This study proposes a feature extraction and Chinese translation vocabulary of IoT English terms based on LSTM, which directly realizes the process of IoT English term feature extraction and Chinese translation at one time, avoiding the complicated design and migration process in the middle, and can effectively guarantee the accuracy of feature extraction of Internet-of-Things English terminology meets the requirements, and the time series-based feature extraction and learning of the model is realized by using the LSTM structure.


## 2. Related Work

### 2.1. IoT English Terminology

The Internet of Things is an emerging field of science and technology in recent years, and the professional vocabulary in this field has the characteristics of typical scientific and technological texts. The vocabulary it uses has strong computer professional characteristics. Professional vocabulary and terminology in the direction of the Internet of Things are becoming more and more complex. Difficult vocabulary, inconvenient reading and writing, difficult memory, and a high repetition rate of abbreviations are the characteristics of Internet-of-Things English terminology. Abbreviations in the computer field are often used in the Internet of Things, such as IoT, NFC, and other words; however, the abbreviations of these words may have multiple meanings. Usually, these words are difficult to understand correctly through translation software. Users with high computer expertise can correctly understand the meaning of words.


### 2.2. English Term Feature Extraction

English term feature extraction is the basis of many natural language processing applications. Its main function is to extract rich phonetic information of English terms from unstructured text so as to facilitate further computer processing and human understanding. English term feature extraction provides a solid foundation for IoT English term understanding and builds rich text semantic features. Most recent English term feature extraction methods use neural network language models to generate English term textual representations. These models use statistics on the frequency or a probability distribution of English term words in the text and represent the word and word frequency or probability distribution in the form of semantic space to construct text semantic representation features. However, when these traditional text semantic feature representation models are used to understand text semantics, they are easily affected by the context and the vocabulary will be ambiguous.


### 2.3. Chinese Translation of Internet-of-Things Terms in English

The Internet of Things is a branch of the computer profession. A large part of the Internet-of-Things English terms are consistent with computer terms, or the composition of these terms is similar to that of computer terms. Therefore, by referring to the translation of computer terms, some Internet-of-Things English terms are analogized. Firstly, terms, reliability, and accuracy of the results obtained in this way are relatively high, which can ensure the internal consistency and practicability of the translated terms and basically meet the basic requirements for the use and translation of the Internet-of-Things terms. Secondly, the category of Internet-of-Things English terminology and technical English should reflect the characteristics of scientific and technological English when translating Internet-of-Things English terms; that is, the translated vocabulary should have a professional vocabulary and rigorous logic.According to whether there is a standardized translation of the Internet-of-Things terms, the English terms of the Internet of Things are roughly divided into two categories, which are the standardized English terms of the Internet of Things and the unregulated English terms of the Internet of Things. Determine the corresponding Chinese translation method. The already standardized Internet-of-Things English is mainly divided into three categories, namely, acronyms, compound words, and semitechnical words. For this type of IoT English terminology, its translation is basically determined, and it has been widely followed and used in the industry. The focus is to summarize this type of method from the normative translation to ensure the accuracy of the translation. For unregulated IoT English terms, the translation situation is more complicated, and it is necessary to combine the user’s IoT expertise, standardized translation methods, and academic discussions to jointly ensure the certainty, accuracy, and reliability of IoT English readability.


## 2.1. IoT English Terminology

The Internet of Things is an emerging field of science and technology in recent years, and the professional vocabulary in this field has the characteristics of typical scientific and technological texts. The vocabulary it uses has strong computer professional characteristics. Professional vocabulary and terminology in the direction of the Internet of Things are becoming more and more complex. Difficult vocabulary, inconvenient reading and writing, difficult memory, and a high repetition rate of abbreviations are the characteristics of Internet-of-Things English terminology. Abbreviations in the computer field are often used in the Internet of Things, such as IoT, NFC, and other words; however, the abbreviations of these words may have multiple meanings. Usually, these words are difficult to understand correctly through translation software. Users with high computer expertise can correctly understand the meaning of words.


## 2.2. English Term Feature Extraction

English term feature extraction is the basis of many natural language processing applications. Its main function is to extract rich phonetic information of English terms from unstructured text so as to facilitate further computer processing and human understanding. English term feature extraction provides a solid foundation for IoT English term understanding and builds rich text semantic features. Most recent English term feature extraction methods use neural network language models to generate English term textual representations. These models use statistics on the frequency or a probability distribution of English term words in the text and represent the word and word frequency or probability distribution in the form of semantic space to construct text semantic representation features. However, when these traditional text semantic feature representation models are used to understand text semantics, they are easily affected by the context and the vocabulary will be ambiguous.


## 2.3. Chinese Translation of Internet-of-Things Terms in English

The Internet of Things is a branch of the computer profession. A large part of the Internet-of-Things English terms are consistent with computer terms, or the composition of these terms is similar to that of computer terms. Therefore, by referring to the translation of computer terms, some Internet-of-Things English terms are analogized. Firstly, terms, reliability, and accuracy of the results obtained in this way are relatively high, which can ensure the internal consistency and practicability of the translated terms and basically meet the basic requirements for the use and translation of the Internet-of-Things terms. Secondly, the category of Internet-of-Things English terminology and technical English should reflect the characteristics of scientific and technological English when translating Internet-of-Things English terms; that is, the translated vocabulary should have a professional vocabulary and rigorous logic.According to whether there is a standardized translation of the Internet-of-Things terms, the English terms of the Internet of Things are roughly divided into two categories, which are the standardized English terms of the Internet of Things and the unregulated English terms of the Internet of Things. Determine the corresponding Chinese translation method. The already standardized Internet-of-Things English is mainly divided into three categories, namely, acronyms, compound words, and semitechnical words. For this type of IoT English terminology, its translation is basically determined, and it has been widely followed and used in the industry. The focus is to summarize this type of method from the normative translation to ensure the accuracy of the translation. For unregulated IoT English terms, the translation situation is more complicated, and it is necessary to combine the user’s IoT expertise, standardized translation methods, and academic discussions to jointly ensure the certainty, accuracy, and reliability of IoT English readability.


## 3. Network Models

The long short-term memory network (LSTM) is an improved recurrent neural network commonly used at present. It can not only solve the problem that recurrent neural networks cannot handle long-distance dependencies but also solve the common model gradient disappearance or gradient explosion problem in neural networks. It is very important to deal with sequence data. This study adopts the network structure based on LSTM and CNN to realize the functions of feature extraction and Chinese translation of Internet-of-Things English terms.The purpose of constructing based on the semantic network is to establish the connection between the multiunderstanding IoT English term text and the additional knowledge, that is, the knowledge base or semantic background knowledge. The knowledge base includes concepts, entities, and connections among entities. When the relational network is rich enough, a rich Internet-of-Things English term feature network can be formed. Usually, the text feature extraction network is generally divided into three steps: word segmentation, academic word part-of-speech tagging, and belonging word recognition, and each step uses a new model for disambiguation in each step. Since Google released the pretrained model BERF, this NLP-based network model has been pretrained and fine-tuned to achieve excellent results on a variety of natural language processing tasks. The BERT network model requires unsupervised training on large-scale data and then fine-tuning on different types of more specialized datasets according to different natural language processing tasks. The idea of the network model we proposed is basically similar to that of BERT. It is also trained on a large natural language processing dataset to obtain a pretrained network model and then fine-tuned on the specific small dataset in this study. On the one hand, it is more suitable for the task of feature extraction and Chinese translation of Internet-of-Things English terms in this study, so as to ensure that the model has a better training effect; on the other hand, debugging on a small dataset can effectively reduce the time and cost of model training computing resources.


### 3.1. LSTM Cell Structure

The full name of LSTM is long short-term memory, which is a neural network with the ability to memorize long- and short-term information. With the rise and development of deep learning, a more systematic and complete LSTM framework has been formed, and it has been widely used in many fields. LSTM introduces a gating mechanism gate to control the circulation and loss of features to solve the long-term dependence of RNN. This study uses the most basic LSTM network structural unit and does not consider its variants.The core structure of LSTM is shown in Figure1. The LSTM network structure in Figure 1 is a two-layer distribution, and the structure diagram is the data transmission direction of multiple LSTM units. An LSTM cell has three gates: forget gate, input gate, and output gate. The final output of the LSTM cell is ht and ct, and its input is ct−1, ht−1, and xt:(1)Ct=ft×Ct−1+it×Ct˜,ft=σWf⋅ht−1,xt+bf,where ft is called the “forget gate,” which means that the features of Ct−1 are used to calculate Ct. Sigmoid is a vector whose value range is between [0, 1]. Usually sigmoid is used as the activation function, and the output of sigmoid is a value in the interval [0, 1]. ⨂ is the most important gate mechanism of LSTM, which represents the unit multiplication relationship between ft and Ct−1:(2)it=σWi⋅ht−1,xt+bi,Ct˜=tanhWc⋅ht−1,xt+bC,where Ct˜ represents the unit state update value, which is obtained from the input data xt and the hidden node ht−1 through a neural network layer, and the activation function of the unit state update usually uses tanh. it is called the input gate, and its value threshold is a vector between [0, 1], which is also calculated from the input data xt and the hidden node ht−1 through the activation function sigmoid:(3)ot=σW0ht−1,xt+b0,ht=ot∗tanhCt.Figure 1
LSTM cell structure.Among them, in order to calculate the predicted valueyt^ and generate the complete input of the next time slice, the output ht of the hidden node needs to be calculated. ht is obtained from the output gate ot and the cell state Ct, where ot is calculated in the same way as ft and it.


### 3.2. LSTM-Based Network Model

RNN, termed a time-series network, can store historical information, but there will be a problem of gradient disappearance when the sequence is too long. As a special form of RNN, LSTM can effectively deal with this problem. The network structure based on LSTM is shown in Figure2. The above network structure includes an LSTM network with two hidden layers. At a single time T, it is an ordinary backpropagation neural network, but after expanding along the time axis, the hidden layer information trained at T = 1 will be passed to the next. At time T = 2, there are five rightward arrows in Figure 2, indicating that the state information of the hidden layer is transmitted on the time axis. Multiple time-series lines represent the values of the two inputs and the values of the three outputs in the LSTM structure, which are embodied in Section 3.1.Figure 2
LSTM network structure.There are many ways to understand text features, but generally, there are four types: input layer, hidden layer, output layer, and time series. The main function of the input layer is to represent each word of the text or IoT English term vocabulary with the word vector of the pretrained model. The hidden layer is to continuously learn the characteristics of the professional vocabulary of the Internet of Things through the established neural network structure and to control the transmission and flow of the characteristics of the intermediate model. The output layer is to output the vocabulary and relations of the table according to the requirements of the model and the format of the output label. The time series mainly deals with the representation of words in time series, focusing on learning the relationship between words.


### 3.3. Feature Extraction of Internet of Things English Terminology and Neural Network for Chinese Translation

In this study, the feature extraction and Chinese translation neural network structure of the Internet of Things English terminology are shown in Figure3. The input data in this paper are the feature dimension x; the length of the vector after the vocabulary is encoded. There are two layers in the middle hidden layer in the network, and the feature dimension of each layer; that is, the number of neurons in the hidden layer is 5. In the structure of the neural network that we designed, a bidirectional recurrent neural network is used. When using LSTM, both forward propagation and backpropagation have output feature data. The output dimension of bidirectional LSTM is twice the number of hidden layer features. The input layer is to represent each word of the text and question with a pretrained word vector. The attention layer uses a bidirectional LSTM attention mechanism to process the time series-based features. The decoding layer is the output of vocabulary and relations and calculates the output probability for the vocabulary and input. The probability of each word being output at the current position is the sum of the probability of being selected in the vocabulary and the probability of being copied in the input. CNN uses ResNet-50 to extract the language features of time series. The ResNet series adopts the basic bottleneck module, which improves the learning ability of features by continuously reducing the input feature size of the network model and increasing the feature dimension.Figure 3
Terminology feature extraction and Chinese translation network.The LSTM-based neural network model does not depend on a specific framework. In this study, we use the LSTM-based encoding and decoding framework. The encoding framework is an overall model for feature extraction, and its main function is to solve the task of feature extraction for Internet-of-Things English terms. First, briefly introduce the encoding and decoding model, such as the feature extraction task of Internet-of-Things English terminology, which is essentially a multilabel classification problem and can be expressed in the form of <sentence, relation label>. The task goal is to generate a sentence of a given Internet English term and generate the label of the specific relationship of the lexical sentence through the encoder-decoder model. In this study, the sentence is regarded as a given resource, and the relationship label is regarded as the lexical sentence relationship label for generating the target vocabulary. Bi-LSTM represents the bidirectional LSTM network structure. The previous sections are all about simple single-layer LSTM network structures. The bidirectional LSTM structure can transmit features in both directions through time series and has better learning ability:(4)Source=w1,w2,…,wm,Target=r1,r2,…,rn.Among them,w1, w2,…, andwm represent the word sequence contained in the current sentence and r1,r2,…,rn represent the relation sequence. In the encoding part, the input sentence source is encoded; that is, the intermediate hidden semantic representation E is obtained through nonlinear transformation:(5)E=fw1,w2,…,wm.The decoding part, whose goal is to select the desired relation according to the intermediate semantic representationE and the relation, lists(6)ri=gE,r1,r2,…,rn.The neural network model based on LSTM proposed in this study is mainly used for the task of feature extraction and Chinese translation of English terminology in the Internet of Things. It solves two problems. One is the statistical language model, which is necessary to calculate a certain probability distribution of vocabulary or technical terms; another problem is the expression of word vectors concerned by the vector space model, that is, the problem of text representation. By adopting the continuous word vector assumption and smooth probability distribution model of the previous work and by modeling the probability distribution of words in the text sequence in a continuous space, the LSTM-based neural network model framework simultaneously obtains the word vector of the word expression and the probability distribution, thereby alleviating the problem of gradient disappearance or gradient explosion. And because of the continuous vector representation method, the data-sparse problem has been alleviated to a certain extent. The main reference object we set this unit is the prediction accuracy of the model. We have set a different number of units, but the setting of 5 balances the accuracy and speed of the model.


## 3.1. LSTM Cell Structure

The full name of LSTM is long short-term memory, which is a neural network with the ability to memorize long- and short-term information. With the rise and development of deep learning, a more systematic and complete LSTM framework has been formed, and it has been widely used in many fields. LSTM introduces a gating mechanism gate to control the circulation and loss of features to solve the long-term dependence of RNN. This study uses the most basic LSTM network structural unit and does not consider its variants.The core structure of LSTM is shown in Figure1. The LSTM network structure in Figure 1 is a two-layer distribution, and the structure diagram is the data transmission direction of multiple LSTM units. An LSTM cell has three gates: forget gate, input gate, and output gate. The final output of the LSTM cell is ht and ct, and its input is ct−1, ht−1, and xt:(1)Ct=ft×Ct−1+it×Ct˜,ft=σWf⋅ht−1,xt+bf,where ft is called the “forget gate,” which means that the features of Ct−1 are used to calculate Ct. Sigmoid is a vector whose value range is between [0, 1]. Usually sigmoid is used as the activation function, and the output of sigmoid is a value in the interval [0, 1]. ⨂ is the most important gate mechanism of LSTM, which represents the unit multiplication relationship between ft and Ct−1:(2)it=σWi⋅ht−1,xt+bi,Ct˜=tanhWc⋅ht−1,xt+bC,where Ct˜ represents the unit state update value, which is obtained from the input data xt and the hidden node ht−1 through a neural network layer, and the activation function of the unit state update usually uses tanh. it is called the input gate, and its value threshold is a vector between [0, 1], which is also calculated from the input data xt and the hidden node ht−1 through the activation function sigmoid:(3)ot=σW0ht−1,xt+b0,ht=ot∗tanhCt.Figure 1
LSTM cell structure.Among them, in order to calculate the predicted valueyt^ and generate the complete input of the next time slice, the output ht of the hidden node needs to be calculated. ht is obtained from the output gate ot and the cell state Ct, where ot is calculated in the same way as ft and it.


## 3.2. LSTM-Based Network Model

RNN, termed a time-series network, can store historical information, but there will be a problem of gradient disappearance when the sequence is too long. As a special form of RNN, LSTM can effectively deal with this problem. The network structure based on LSTM is shown in Figure2. The above network structure includes an LSTM network with two hidden layers. At a single time T, it is an ordinary backpropagation neural network, but after expanding along the time axis, the hidden layer information trained at T = 1 will be passed to the next. At time T = 2, there are five rightward arrows in Figure 2, indicating that the state information of the hidden layer is transmitted on the time axis. Multiple time-series lines represent the values of the two inputs and the values of the three outputs in the LSTM structure, which are embodied in Section 3.1.Figure 2
LSTM network structure.There are many ways to understand text features, but generally, there are four types: input layer, hidden layer, output layer, and time series. The main function of the input layer is to represent each word of the text or IoT English term vocabulary with the word vector of the pretrained model. The hidden layer is to continuously learn the characteristics of the professional vocabulary of the Internet of Things through the established neural network structure and to control the transmission and flow of the characteristics of the intermediate model. The output layer is to output the vocabulary and relations of the table according to the requirements of the model and the format of the output label. The time series mainly deals with the representation of words in time series, focusing on learning the relationship between words.


## 3.3. Feature Extraction of Internet of Things English Terminology and Neural Network for Chinese Translation

In this study, the feature extraction and Chinese translation neural network structure of the Internet of Things English terminology are shown in Figure3. The input data in this paper are the feature dimension x; the length of the vector after the vocabulary is encoded. There are two layers in the middle hidden layer in the network, and the feature dimension of each layer; that is, the number of neurons in the hidden layer is 5. In the structure of the neural network that we designed, a bidirectional recurrent neural network is used. When using LSTM, both forward propagation and backpropagation have output feature data. The output dimension of bidirectional LSTM is twice the number of hidden layer features. The input layer is to represent each word of the text and question with a pretrained word vector. The attention layer uses a bidirectional LSTM attention mechanism to process the time series-based features. The decoding layer is the output of vocabulary and relations and calculates the output probability for the vocabulary and input. The probability of each word being output at the current position is the sum of the probability of being selected in the vocabulary and the probability of being copied in the input. CNN uses ResNet-50 to extract the language features of time series. The ResNet series adopts the basic bottleneck module, which improves the learning ability of features by continuously reducing the input feature size of the network model and increasing the feature dimension.Figure 3
Terminology feature extraction and Chinese translation network.The LSTM-based neural network model does not depend on a specific framework. In this study, we use the LSTM-based encoding and decoding framework. The encoding framework is an overall model for feature extraction, and its main function is to solve the task of feature extraction for Internet-of-Things English terms. First, briefly introduce the encoding and decoding model, such as the feature extraction task of Internet-of-Things English terminology, which is essentially a multilabel classification problem and can be expressed in the form of <sentence, relation label>. The task goal is to generate a sentence of a given Internet English term and generate the label of the specific relationship of the lexical sentence through the encoder-decoder model. In this study, the sentence is regarded as a given resource, and the relationship label is regarded as the lexical sentence relationship label for generating the target vocabulary. Bi-LSTM represents the bidirectional LSTM network structure. The previous sections are all about simple single-layer LSTM network structures. The bidirectional LSTM structure can transmit features in both directions through time series and has better learning ability:(4)Source=w1,w2,…,wm,Target=r1,r2,…,rn.Among them,w1, w2,…, andwm represent the word sequence contained in the current sentence and r1,r2,…,rn represent the relation sequence. In the encoding part, the input sentence source is encoded; that is, the intermediate hidden semantic representation E is obtained through nonlinear transformation:(5)E=fw1,w2,…,wm.The decoding part, whose goal is to select the desired relation according to the intermediate semantic representationE and the relation, lists(6)ri=gE,r1,r2,…,rn.The neural network model based on LSTM proposed in this study is mainly used for the task of feature extraction and Chinese translation of English terminology in the Internet of Things. It solves two problems. One is the statistical language model, which is necessary to calculate a certain probability distribution of vocabulary or technical terms; another problem is the expression of word vectors concerned by the vector space model, that is, the problem of text representation. By adopting the continuous word vector assumption and smooth probability distribution model of the previous work and by modeling the probability distribution of words in the text sequence in a continuous space, the LSTM-based neural network model framework simultaneously obtains the word vector of the word expression and the probability distribution, thereby alleviating the problem of gradient disappearance or gradient explosion. And because of the continuous vector representation method, the data-sparse problem has been alleviated to a certain extent. The main reference object we set this unit is the prediction accuracy of the model. We have set a different number of units, but the setting of 5 balances the accuracy and speed of the model.


## 4. Experimental Results and Analysis

### 4.1. Dataset and Related Settings

In the experiment, we use the Wikipedia corpus for training to obtain word vectors and use the Twitter phrase text dataset and the established IoT English term dataset for training and testing. The results of each type of experiment are different mainly because the indicators corresponding to different characters are different. In order to compare this study, this study designs a unified comparison index.The precision rate P, recall rateR, and F1 values used in the study are used as the evaluation indicators of the model, and their calculation formulas are as follows:(7)P=Extract the correct number of keywordsThe number of all keywords extracted,R=Extract the correct number of keywordsThe number of all keywords in the text,F1=2∗P∗RP+R.


### 4.2. Experimental Results and Analysis

The number and accuracy of text features extracted by the network model proposed in this paper are shown in Figure4. The median of the word vector in the extracted data is basically the same as the original label, and the extraction of each IoT English term is relatively accurate, which basically meets the extraction requirements of English term words.Figure 4
Precision analysis of different sampling groups.In order to prove the effectiveness of the network model proposed in this study in learning the features of the Internet-of-Things English term features with time series, we learned the word features with time series, and the experimental results are shown in Figure5. Among them, A, B, C, and D represent four types of IoT professional terms, which are abbreviations, standard words, literal translations, and ellipsis. These different types of IoT English terminology professional vocabulary are manually annotated, and the data input to the network is the text data containing these features. By comparing these words, we can comprehensively evaluate the actual performance of the model. Through these labeled words, the performance of the model is evaluated from four aspects: abbreviations, standard words, literal translations, and ellipsis.Figure 5
Four different characteristic parameters change over time.The recall rate, F1 value, and accuracy P of the model are shown in A, B, and C in Figure6. The result of its change is mainly the text data currently collected and sampled. These three parameters are mainly used to describe the performance of the network model. The x value in the figure represents the number of times the network model is trained, that is, the continuous training process of the network model. The change process is mainly affected by the number of model training times; that is, the model adjusts and improves the model weights and values of the entire network in the continuous learning process so that the learning effect continues to be promoted.Figure 6
Comparison of the prediction effects of the three methods.Figure7 shows the change in recall of images. On the whole, with the increase of the number of Internet-of-Things English term keywords, the recall rate of the model tends to increase, and with the continuous increase, the recall rate of the model also decreases. We can indeed provide some useful information after artificially increasing the confidence information of words, and with the increase of the number of keywords, the characteristics of the model will continue to improve to a certain level. As the number of words increases and lexical confidence information increases, the network model exhibits improved recall.Figure 7
Comparison of the effects of various factors.Figure8 shows the change of the F1 value of the model. It is mainly affected by the number of keywords in the English terminology of IoT and the corresponding time series. The main variable under these conditions are the number of keywords in the English terminology of the Internet of Things and the corresponding time-series length. It can be clearly seen that the F1 value of the model has obvious periodic changes. The change determines the length of the model’s processing time series.Figure 8
Comparison of the effects of various factors.Figure9 shows the variation of the accuracy of the model. It is mainly affected by the word count and corresponding sampling rate of IoT English terms. The main variable conditions are the number of words in IoT English terms and the corresponding sampling rate. To a certain extent, the prediction accuracy of the model can be effectively improved by increasing the sampling rate and the number of words of the model. After a certain range is exceeded, the performance of the model will decrease accordingly. Generally speaking, a moderate sampling rate and the number of words of the model should be maintained.Figure 9
Comparison of the effects of various factors.Figure10 shows the confusion matrix of model recognition, IoT English term feature extraction, and Chinese translation. The value of the diagonal line represents the accuracy of recognition, and the larger the value, the higher the accuracy of recognition. At the same time, from the matrix, we can find that there is a recognition error, and the word relationship 1 is recognized as 2. In the experiments in this study, we mainly verify the actual prediction accuracy of the network model. Therefore, we divide the classification level into 5 categories, which are correct, similar, general, different, and wrong. Corresponding to each category, we quantitatively score it with numerical values, which shows that the effect of our network model can meet the requirements as a whole.Figure 10
The prediction accuracy of the network for different types of features.


## 4.1. Dataset and Related Settings

In the experiment, we use the Wikipedia corpus for training to obtain word vectors and use the Twitter phrase text dataset and the established IoT English term dataset for training and testing. The results of each type of experiment are different mainly because the indicators corresponding to different characters are different. In order to compare this study, this study designs a unified comparison index.The precision rate P, recall rateR, and F1 values used in the study are used as the evaluation indicators of the model, and their calculation formulas are as follows:(7)P=Extract the correct number of keywordsThe number of all keywords extracted,R=Extract the correct number of keywordsThe number of all keywords in the text,F1=2∗P∗RP+R.


## 4.2. Experimental Results and Analysis

The number and accuracy of text features extracted by the network model proposed in this paper are shown in Figure4. The median of the word vector in the extracted data is basically the same as the original label, and the extraction of each IoT English term is relatively accurate, which basically meets the extraction requirements of English term words.Figure 4
Precision analysis of different sampling groups.In order to prove the effectiveness of the network model proposed in this study in learning the features of the Internet-of-Things English term features with time series, we learned the word features with time series, and the experimental results are shown in Figure5. Among them, A, B, C, and D represent four types of IoT professional terms, which are abbreviations, standard words, literal translations, and ellipsis. These different types of IoT English terminology professional vocabulary are manually annotated, and the data input to the network is the text data containing these features. By comparing these words, we can comprehensively evaluate the actual performance of the model. Through these labeled words, the performance of the model is evaluated from four aspects: abbreviations, standard words, literal translations, and ellipsis.Figure 5
Four different characteristic parameters change over time.The recall rate, F1 value, and accuracy P of the model are shown in A, B, and C in Figure6. The result of its change is mainly the text data currently collected and sampled. These three parameters are mainly used to describe the performance of the network model. The x value in the figure represents the number of times the network model is trained, that is, the continuous training process of the network model. The change process is mainly affected by the number of model training times; that is, the model adjusts and improves the model weights and values of the entire network in the continuous learning process so that the learning effect continues to be promoted.Figure 6
Comparison of the prediction effects of the three methods.Figure7 shows the change in recall of images. On the whole, with the increase of the number of Internet-of-Things English term keywords, the recall rate of the model tends to increase, and with the continuous increase, the recall rate of the model also decreases. We can indeed provide some useful information after artificially increasing the confidence information of words, and with the increase of the number of keywords, the characteristics of the model will continue to improve to a certain level. As the number of words increases and lexical confidence information increases, the network model exhibits improved recall.Figure 7
Comparison of the effects of various factors.Figure8 shows the change of the F1 value of the model. It is mainly affected by the number of keywords in the English terminology of IoT and the corresponding time series. The main variable under these conditions are the number of keywords in the English terminology of the Internet of Things and the corresponding time-series length. It can be clearly seen that the F1 value of the model has obvious periodic changes. The change determines the length of the model’s processing time series.Figure 8
Comparison of the effects of various factors.Figure9 shows the variation of the accuracy of the model. It is mainly affected by the word count and corresponding sampling rate of IoT English terms. The main variable conditions are the number of words in IoT English terms and the corresponding sampling rate. To a certain extent, the prediction accuracy of the model can be effectively improved by increasing the sampling rate and the number of words of the model. After a certain range is exceeded, the performance of the model will decrease accordingly. Generally speaking, a moderate sampling rate and the number of words of the model should be maintained.Figure 9
Comparison of the effects of various factors.Figure10 shows the confusion matrix of model recognition, IoT English term feature extraction, and Chinese translation. The value of the diagonal line represents the accuracy of recognition, and the larger the value, the higher the accuracy of recognition. At the same time, from the matrix, we can find that there is a recognition error, and the word relationship 1 is recognized as 2. In the experiments in this study, we mainly verify the actual prediction accuracy of the network model. Therefore, we divide the classification level into 5 categories, which are correct, similar, general, different, and wrong. Corresponding to each category, we quantitatively score it with numerical values, which shows that the effect of our network model can meet the requirements as a whole.Figure 10
The prediction accuracy of the network for different types of features.


## 5. Summary

The Internet-of-Things English term representation model needs to convert the English term text into a form that can be processed by computers, and this form preserves the semantic information and the relationship between the vocabularies between the English texts on the time series to the greatest extent. English term keywords are extracted and translated. This study proposes a neural network based on LSTM for feature extraction and Chinese translation of English terminology in the Internet of Things. The method proposed in this study basically achieves a relatively accurate prediction, which can meet the basic requirements of feature extraction and Chinese translation of Internet-of-Things English terms, and there is still a lot of room for improvement in the subsequent development process. In future work, we will make some improvements to the above problems and design some new methods, such as introducing common sense knowledge and connecting various network models, so that the feature extraction and Chinese translation of IoT English terminology will be more pragmatic and refined direction of penetration.



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*Source: 6344571-2022-04-28.xml*