# Construction of English Intelligent Translation Software Framework Based on Data Analysis Algorithm

Authors: Zhihui Zhou
Journal: Mobile Information Systems
 (2022)

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## Abstract

In the era of “Internet + education,” the information technology and learning methods of college students have become inseparable. The rapid development of intelligent translation software can provide convenience for English-Chinese translation and computer English learning and simultaneously improve the quality and efficiency of professional English learning. Under such background, the English teaching of software majors is chosen as the breakthrough to analyze the measures of applying intelligent translation software during the teaching process. Therefore, a data mining algorithm including cluster analysis and BP neural network model is designed. Then, the cluster analysis algorithm is used to classify the data in different forms, which can improve the data utilization efficiency. In addition, K-means algorithm based on feature selection is improved to achieve better performance. In the comparison of translation speed and matching rate, our method is much better than other software.


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## Body

## 1. Introduction

In the English learning of college students, translation tools play an important role, which may not be accurate enough in terms of grammar and sentence patterns. In this regard, the translation software should be updated and improved so that it can be better used in college students’ English learning. The rapid development of intelligent translation software under the background of Internet information technology can help solve the problem of language barriers for translators. Software majors use intelligent translation software for English-Chinese translation and computer English learning and for improving the quality and efficiency of professional English learning.The essence of translation software is machine translation, which is included in the study of machine translation. Machine translation refers to the process of converting one language into another target language through computer-related processing, generally referring to the translation of sentences and full texts between different languages [1].In the era of Internet + education, translation software, as a kind of machine translation, is quietly changing the way and behavioral habits of college students’ English learning with the help of information technology. College English teaching has important theoretical and practical significance. Using “translation software” and “college English learning” as keywords to search on CNKI, it is found that the research on translation software in our country at this stage mainly focuses on the translation software itself and the development and design of translation software. In general, the two types of research have different focuses. Some research studies focus on the research on translation software itself, such as the current situation and future development trends of translation software. Human translation and machine translation are compared, focusing on the differences between them; some research studies focus on how to develop and design translation software through different technologies and different angles to enrich translation software in practice. It can be used in different aspects of life [2–5], so as to achieve the ultimate goal of improving the effective utilization rate of translation software. Although the focus of the two types of research is different, they both hold a positive attitude towards the future of translation software, hoping that the future development of translation software will be better and better through continuous improvement and perfection.Therefore, it can be seen that under the background of today's Internet + era, in order to make information technology better serve our study and life, further research on translation software is imperative. In addition to the above two types of research, a few scholars have studied and explored the application of translation software and put forward some reference suggestions for reference. Ali et al. [6] used observation and investigation methods to take the undergraduate course of “Technical Translation” of Shanghai Second Polytechnic University as a case study, discussed the application of translation software “Technical Translation” in classroom teaching, and analyzed the problems faced in the practical application of translation software. Reference [3] put forward methods to try to solve problems such as school enterprise cooperation and teacher training. Lei and Shao [4] combined translation software with editing work and discussed how to look at translation software from the perspective of editing, especially the application of translation software.Through the analysis of the above-mentioned literature, the actual use of translation software, the behavioral characteristics, and tendencies are explored as the research starting point. The corresponding recommendations are rarely studied. Therefore, in this work, the English intelligent translation software based on data analysis algorithm is designed to provide inspiration and suggestions for college English education [7].


## 2. Designing Big Data Analysis Architecture

The design is shown in Figure1.Figure 1
Architecture design of big data analysis.The data acquisition layer collects a large amount of heterogeneous data from the Internet and converts the collected data into well structured data [8–10].


## 3. Data Mining Algorithms

An analysis process becomes a class of similar objects (see Figure2).Figure 2
Data mining algorithm flow.


### 3.1. Cluster Analysis Algorithm

By clustering thek sample data points in the space as the center, the information of big data which is closest to different samples is finally classified [11–14].The cluster analysis algorithm flow is shown in Figure3.Figure 3
Cluster analysis algorithm flow.


## 3.1. Cluster Analysis Algorithm

By clustering thek sample data points in the space as the center, the information of big data which is closest to different samples is finally classified [11–14].The cluster analysis algorithm flow is shown in Figure3.Figure 3
Cluster analysis algorithm flow.


## 4. K-Means Feature Selection Algorithm

According to the research and status analysis of data mining and clustering algorithm in the paper, the author has consulted a large number of studies and conducted in-depth research on K-means algorithm. Based on the research and use of the K-means algorithm by researchers in recent years, this paper improves the algorithm, and finally, an algorithm based on feature selection is used [15]. The algorithm first sets the corresponding feature attributes for college students, then filters and cleans irrelevant feature attributes, normalizes the selection of corresponding feature attributes, and then assigns initial values to the cluster centers and updates them continuously.


### 4.1. Feature Selection Methods

When the collected data object set has too many feature attributes, some invalid or repeated feature attributes will be mixed, which will increase the complexity of cluster analysis, reduce the performance of the algorithm, and even affect the accuracy of the calculation results. To solve this problem, filtering and cleaning invalid feature attributes is necessary. In the paper, we judge the contribution of a feature attribute to the current cluster by calculating the value of the weight vector of the feature attribute. At the same time, we measure the contribution of a feature to clustering by examining and verifying the difference of feature attributes between data objects of the same category and data objects of different categories [16]. If the feature attributes are obviously different from each other between objects of different categories but are not clearly distinguished from each other between objects of the same data category, that is, the weight of the feature attribute is larger, then the feature attribute has a high degree of contribution to the clustering, with strong feature discrimination ability. When the algorithm starts to calculate, it first randomly sets a data object Si as the centroid, then divides the data set into categories, and then selects d data objects with a distance Si from each category of data objects. d data objects of the same category as Si constitute a new dataset T(c), and objects of different categories from Si constitute a new set G according to their category (c is updated according to sets T(c) and G(c)). The weight vector W = {w1, w2, ..., wq} of the feature attribute; then, the calculation formula of the feature attribute weight is shown in the following formula [17]:(1)Wti+1=Wti−∑x∈Tcdifft,Si,xn∗d+∑x∈SSiqc/1−qSSi∑x∈Gcdifft,Si,xn∗d.Among them,n is the number of times to extract sample data, and the diff(t, Si, x) function represents the difference function of the feature attribute of the data object on t. The calculation method is as follows:(2)difft,Si,x=Sit−Sjtmaxt−mint.This method will balance thed data objects with similar distances to Si by using the maximum and minimum values in the feature attributes and then multiply the proportion of other data objects in the dataset in all the data objects of different categories from Si, so the difference between the data object Si and the category is obtained, so as to evaluate the contribution of the feature attribute to the data object.


### 4.2. Optimal Selection of Initial Cluster Centers

The Euclidean distance between any two data objectsxi and xj(1<i < j < n) is defined as follows [6]:(3)dxi,xj=∑a=1pxia−xja2,(4)densityxi=∑j=1ndxi,xj∑i=1ndxi,xj.Assume that the neighborhood radiusRi of the data object xi(1 ≤ i ≤ n) in the dataset can be defined as shown in formula (5): where cR (0 < cR < 1) is used as the adjustment coefficient of the neighborhood radius, according to past experience. It shows that the clustering effect will be better when the value of cR is 0.13 [15].(5)Ri=ncR×1n∑i=1ne−densityxi.The larger the value ofD(Xi), the higher the density of points in the spherical area where the data object is located, as shown in the following formula:(6)Dxi=p|dxi,p≤Ri,p∈X.LetMD(x) be the average density of data objects in dataset X, as shown in the following equation:(7)MDx=1n∑x∈XDx.The specific steps for selecting and optimizing the initial cluster center are as follows.Input: the initial datasetX and the specific number of clusters k.(1)
Calculate the distance between any two data objects inX using formula (5) and then form a distance matrix by these distance values.(2)
According to the distance density function (6), the neighborhood radius calculation formula (7), and the point density calculation formula (5), obtain the point density D(Xi) corresponding to each data object.(3)
According to the mean density calculation formula (7), the mean density of the dataset S is calculated as MD(x).(4)
Calculate the point density of each data object obtained and then compare, and divide all data objects not less than the average density into a setM [14].(5)
Arrange the data objects in the setM in descending order according to the point density.(6)
Select a data object whose point density is only less than the data object obtained in step (6).According to the above process, the corresponding flowchart of the algorithm is shown in Figure4.Figure 4
Flowchart of initial cluster center selection.


## 4.1. Feature Selection Methods

When the collected data object set has too many feature attributes, some invalid or repeated feature attributes will be mixed, which will increase the complexity of cluster analysis, reduce the performance of the algorithm, and even affect the accuracy of the calculation results. To solve this problem, filtering and cleaning invalid feature attributes is necessary. In the paper, we judge the contribution of a feature attribute to the current cluster by calculating the value of the weight vector of the feature attribute. At the same time, we measure the contribution of a feature to clustering by examining and verifying the difference of feature attributes between data objects of the same category and data objects of different categories [16]. If the feature attributes are obviously different from each other between objects of different categories but are not clearly distinguished from each other between objects of the same data category, that is, the weight of the feature attribute is larger, then the feature attribute has a high degree of contribution to the clustering, with strong feature discrimination ability. When the algorithm starts to calculate, it first randomly sets a data object Si as the centroid, then divides the data set into categories, and then selects d data objects with a distance Si from each category of data objects. d data objects of the same category as Si constitute a new dataset T(c), and objects of different categories from Si constitute a new set G according to their category (c is updated according to sets T(c) and G(c)). The weight vector W = {w1, w2, ..., wq} of the feature attribute; then, the calculation formula of the feature attribute weight is shown in the following formula [17]:(1)Wti+1=Wti−∑x∈Tcdifft,Si,xn∗d+∑x∈SSiqc/1−qSSi∑x∈Gcdifft,Si,xn∗d.Among them,n is the number of times to extract sample data, and the diff(t, Si, x) function represents the difference function of the feature attribute of the data object on t. The calculation method is as follows:(2)difft,Si,x=Sit−Sjtmaxt−mint.This method will balance thed data objects with similar distances to Si by using the maximum and minimum values in the feature attributes and then multiply the proportion of other data objects in the dataset in all the data objects of different categories from Si, so the difference between the data object Si and the category is obtained, so as to evaluate the contribution of the feature attribute to the data object.


## 4.2. Optimal Selection of Initial Cluster Centers

The Euclidean distance between any two data objectsxi and xj(1<i < j < n) is defined as follows [6]:(3)dxi,xj=∑a=1pxia−xja2,(4)densityxi=∑j=1ndxi,xj∑i=1ndxi,xj.Assume that the neighborhood radiusRi of the data object xi(1 ≤ i ≤ n) in the dataset can be defined as shown in formula (5): where cR (0 < cR < 1) is used as the adjustment coefficient of the neighborhood radius, according to past experience. It shows that the clustering effect will be better when the value of cR is 0.13 [15].(5)Ri=ncR×1n∑i=1ne−densityxi.The larger the value ofD(Xi), the higher the density of points in the spherical area where the data object is located, as shown in the following formula:(6)Dxi=p|dxi,p≤Ri,p∈X.LetMD(x) be the average density of data objects in dataset X, as shown in the following equation:(7)MDx=1n∑x∈XDx.The specific steps for selecting and optimizing the initial cluster center are as follows.Input: the initial datasetX and the specific number of clusters k.(1)
Calculate the distance between any two data objects inX using formula (5) and then form a distance matrix by these distance values.(2)
According to the distance density function (6), the neighborhood radius calculation formula (7), and the point density calculation formula (5), obtain the point density D(Xi) corresponding to each data object.(3)
According to the mean density calculation formula (7), the mean density of the dataset S is calculated as MD(x).(4)
Calculate the point density of each data object obtained and then compare, and divide all data objects not less than the average density into a setM [14].(5)
Arrange the data objects in the setM in descending order according to the point density.(6)
Select a data object whose point density is only less than the data object obtained in step (6).According to the above process, the corresponding flowchart of the algorithm is shown in Figure4.Figure 4
Flowchart of initial cluster center selection.


## 5. Test Analysis

### 5.1. Setting Test Parameters

The network multilingual timely translation systems in [4, 7] were introduced, and the test parameters were set, as shown in Table 1.Table 1
Test parameters.
Parameter nameNumerical sizeInternet multilingual words translation volume/characters200Translation rate (Kb/s)10Internet multilingual words translation volume/characters400Translation rate (Kb/s)10The translation system test experiment needs to pay attention to the randomness of the test object selection. In order to ensure the accuracy of the whole experiment process, it is necessary to strictly limit the conditions of the experimental object (see Table2).Table 2
Experimental data settings.
Test times/every timeSemantic context translation translation typesRevision parameters130.2∗10−3220.1∗10−3330.2∗10−3430.1∗10−3530.2∗10−3620.1∗10−3


### 5.2. Internet Multilingual Translation Speed Test

Taking the number of online multilingual sentences as an independent variable, three translation systems are used to test the speed of online multilingual translation. It can be seen that the network multilingual timely translation system in [4] does not count the network multilingual data in the database in terms of hardware design, so the training data cannot be obtained. The average translation speed during the network multilingual test is 4.275 sentences per second; the performance of the network multilingual timely translation system in [7] is relatively better than that of the network multilingual translation system in [4], but due to the inability to extract the semantic features of network multilanguage, the translation of network multilanguage becomes more complicated. After calculation, the average translation speed in the process of network multilanguage test is 5.566 sentences per second; based on data, the network multilanguage timely translation system of the English intelligent translation software based on the analysis algorithm combines the software and hardware advantages of the above two systems to speed up the translation speed of network multilanguage. After calculation, the average translation speed in the network multilanguage test process is 1 second (see Table 3).Table 3
Comparison results of network multilingual translation sentences speed.
Number of sentencesReference [4]Reference [7]Ours1 0002.835.246.932 0003.045.517.043 0003.245.587.684 0003.685.657.655 0004.185.728.136 0004.465.958.527 0004.982.248.758 0005.036.428.949 0005.426.619.2310 0005.896.7410.53


### 5.3. Network Multilingual Matching Rate Test

The online multilingual timely translation systems in [4, 7] and the English intelligent translation system based on data analysis algorithms are used, respectively. The software’s network multilanguage timely translation system tests the matching rate of network multilanguage (see Figure 5).Figure 5
Test results of network multilingual matching rate.From the test results in Figure5, that there are many English intelligent translation software based on the data analysis algorithm, and the matching rate of the language real-time translation system is the highest.


## 5.1. Setting Test Parameters

The network multilingual timely translation systems in [4, 7] were introduced, and the test parameters were set, as shown in Table 1.Table 1
Test parameters.
Parameter nameNumerical sizeInternet multilingual words translation volume/characters200Translation rate (Kb/s)10Internet multilingual words translation volume/characters400Translation rate (Kb/s)10The translation system test experiment needs to pay attention to the randomness of the test object selection. In order to ensure the accuracy of the whole experiment process, it is necessary to strictly limit the conditions of the experimental object (see Table2).Table 2
Experimental data settings.
Test times/every timeSemantic context translation translation typesRevision parameters130.2∗10−3220.1∗10−3330.2∗10−3430.1∗10−3530.2∗10−3620.1∗10−3


## 5.2. Internet Multilingual Translation Speed Test

Taking the number of online multilingual sentences as an independent variable, three translation systems are used to test the speed of online multilingual translation. It can be seen that the network multilingual timely translation system in [4] does not count the network multilingual data in the database in terms of hardware design, so the training data cannot be obtained. The average translation speed during the network multilingual test is 4.275 sentences per second; the performance of the network multilingual timely translation system in [7] is relatively better than that of the network multilingual translation system in [4], but due to the inability to extract the semantic features of network multilanguage, the translation of network multilanguage becomes more complicated. After calculation, the average translation speed in the process of network multilanguage test is 5.566 sentences per second; based on data, the network multilanguage timely translation system of the English intelligent translation software based on the analysis algorithm combines the software and hardware advantages of the above two systems to speed up the translation speed of network multilanguage. After calculation, the average translation speed in the network multilanguage test process is 1 second (see Table 3).Table 3
Comparison results of network multilingual translation sentences speed.
Number of sentencesReference [4]Reference [7]Ours1 0002.835.246.932 0003.045.517.043 0003.245.587.684 0003.685.657.655 0004.185.728.136 0004.465.958.527 0004.982.248.758 0005.036.428.949 0005.426.619.2310 0005.896.7410.53


## 5.3. Network Multilingual Matching Rate Test

The online multilingual timely translation systems in [4, 7] and the English intelligent translation system based on data analysis algorithms are used, respectively. The software’s network multilanguage timely translation system tests the matching rate of network multilanguage (see Figure 5).Figure 5
Test results of network multilingual matching rate.From the test results in Figure5, that there are many English intelligent translation software based on the data analysis algorithm, and the matching rate of the language real-time translation system is the highest.


## 6. Conclusion

A data mining method is proposed to achieve the classification of data. In such method, clustering analysis algorithm is used to increase the recognition accuracy of various types of data. Then, the efficiency and accuracy of data are further improved by using BP network. Furthermore, such method enables users to precisely improve the accuracy of the data from the clustered big data. In addition, the Apriori algorithm can simplify the connection operation part by comparing and deleting unnecessary connection operations. Because of this, the improved Apriori algorithm saves the cost and reduces the interface load of the data mining platform. In actual use, the optimized algorithm can save a lot of computing time, and the performance is much better than the traditional Apriori algorithm. The application of data mining algorithm in the analysis of college students’ English learning translation software offers great help to future education. However, the negative effects of security information leakage and student behavior not being trusted should also be a cause for concern. Therefore, educators should carefully monitor big data algorithms in the process of promoting data mining technology. They cannot blindly trust the evaluation results from intelligent algorithms and ignore the feelings of students. Teachers can use data mining results as auxiliary plans. Scientific evaluation and prevention of negative factors cannot be overlooked in data mining technology.



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*Source: 7773157-2022-08-31.xml*