Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.06.004
Xin Hu, Yanfei Yang, X. Wu, Yan Li
The traditional teaching quality evaluation methods of colleges and universities have been unable to meet the informatization and modern teaching modes in terms of accuracy and implementation efficiency. Therefore, for the problem of evaluating teaching quality in colleges and universities, this paper proposes a sentiment analysis method for teaching evaluation text based on machine learning. This article establishes a teaching evaluation feature dictionary, reduces the dimensionality of attribute features through mining analysis, and extracts the features most relevant to teacher evaluation. In addition, the support vector machine algorithms with linear kernel, polynomial kernel and radial basis kernel are used to classify the sentiment of the text data in teaching evaluation to judge the sentiment tendency of evaluation. The experimental results show that the support vector machine radial basis kernel has the best effect on the classification of teaching evaluation text data, which can enable teachers to accurately obtain feedback information for evaluation, so that they can adjust their teaching work in time to assist teaching decisions and improve teaching quality.
{"title":"Text Analysis of Teaching Evaluation Based on Machine Learning","authors":"Xin Hu, Yanfei Yang, X. Wu, Yan Li","doi":"10.18178/wcse.2020.06.004","DOIUrl":"https://doi.org/10.18178/wcse.2020.06.004","url":null,"abstract":"The traditional teaching quality evaluation methods of colleges and universities have been unable to meet the informatization and modern teaching modes in terms of accuracy and implementation efficiency. Therefore, for the problem of evaluating teaching quality in colleges and universities, this paper proposes a sentiment analysis method for teaching evaluation text based on machine learning. This article establishes a teaching evaluation feature dictionary, reduces the dimensionality of attribute features through mining analysis, and extracts the features most relevant to teacher evaluation. In addition, the support vector machine algorithms with linear kernel, polynomial kernel and radial basis kernel are used to classify the sentiment of the text data in teaching evaluation to judge the sentiment tendency of evaluation. The experimental results show that the support vector machine radial basis kernel has the best effect on the classification of teaching evaluation text data, which can enable teachers to accurately obtain feedback information for evaluation, so that they can adjust their teaching work in time to assist teaching decisions and improve teaching quality.","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.06.008
{"title":"Augmented Fuzzing with Promotion on Numerical Dependence","authors":"","doi":"10.18178/wcse.2020.06.008","DOIUrl":"https://doi.org/10.18178/wcse.2020.06.008","url":null,"abstract":"","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125473800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.02.004
{"title":"Analysis of Outlier Detection on Structured Data","authors":"","doi":"10.18178/wcse.2020.02.004","DOIUrl":"https://doi.org/10.18178/wcse.2020.02.004","url":null,"abstract":"","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129213185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.06.020
{"title":"An Improved CAM for Weakly Supervised Fabric Defect Detection","authors":"","doi":"10.18178/wcse.2020.06.020","DOIUrl":"https://doi.org/10.18178/wcse.2020.06.020","url":null,"abstract":"","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127156357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.06.051
{"title":"LT-AES: Automatic Academic Paper Evaluation Model","authors":"","doi":"10.18178/wcse.2020.06.051","DOIUrl":"https://doi.org/10.18178/wcse.2020.06.051","url":null,"abstract":"","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115235435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18178/wcse.2020.02.007
T. Oo, Ye Kyaw Thu, K. Soe, T. Supnithi
This paper contributes the first evaluation of the quality of machine translation between Myanmar and Myeik (also known as Beik) . We also developed a Myanmar-Myeik parallel corpus (around 10K sentences) based on the Myanmar language of ASEAN MT corpus. In addition, two types of segmentation were studied word and syllable segmentation. The 10 folds cross-validation experiments were carried out using three different statistical machine translation approaches: phrase-based, hierarchical phrasebased, and the operation sequence model (OSM). The results show that all three statistical machine translation approaches give higher and comparable BLEU and RIBES scores for both Myanmar to Myeik and Myeik to Myanmar machine translations. OSM approach achieved the highest BLEU and RIBES scores among three approaches. We also found that syllable segmentation is appropriate for translation quality comparing with word level segmentation results.
{"title":"Statistical Machine Translation between Myanmar and Myeik","authors":"T. Oo, Ye Kyaw Thu, K. Soe, T. Supnithi","doi":"10.18178/wcse.2020.02.007","DOIUrl":"https://doi.org/10.18178/wcse.2020.02.007","url":null,"abstract":"This paper contributes the first evaluation of the quality of machine translation between Myanmar and Myeik (also known as Beik) . We also developed a Myanmar-Myeik parallel corpus (around 10K sentences) based on the Myanmar language of ASEAN MT corpus. In addition, two types of segmentation were studied word and syllable segmentation. The 10 folds cross-validation experiments were carried out using three different statistical machine translation approaches: phrase-based, hierarchical phrasebased, and the operation sequence model (OSM). The results show that all three statistical machine translation approaches give higher and comparable BLEU and RIBES scores for both Myanmar to Myeik and Myeik to Myanmar machine translations. OSM approach achieved the highest BLEU and RIBES scores among three approaches. We also found that syllable segmentation is appropriate for translation quality comparing with word level segmentation results.","PeriodicalId":292895,"journal":{"name":"Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133163033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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