{"title":"一个支持高中生的聊天机器人意图分类器","authors":"Suha Khalil Assayed, K. Shaalan, M. Alkhatib","doi":"10.4108/eetsis.v10i2.2948","DOIUrl":null,"url":null,"abstract":"INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. \nOBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. \nMETHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. \nRESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. \nCONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"86 1","pages":"e1"},"PeriodicalIF":1.1000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Chatbot Intent Classifier for Supporting High School Students\",\"authors\":\"Suha Khalil Assayed, K. Shaalan, M. Alkhatib\",\"doi\":\"10.4108/eetsis.v10i2.2948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. \\nOBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. \\nMETHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. \\nRESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. \\nCONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.\",\"PeriodicalId\":43034,\"journal\":{\"name\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"volume\":\"86 1\",\"pages\":\"e1\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eetsis.v10i2.2948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Scalable Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetsis.v10i2.2948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A Chatbot Intent Classifier for Supporting High School Students
INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests.
OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice.
METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions.
RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF.
CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.