Muhammad Farhan, M. Iqbal, M. Aslam, A. Enríquez, A. Farooq, Saad Tanveer, A. P. Mejia
Electronic Learning (e-Learning) is used to educate people in these days. Using e-Learning, a number of world ranking universities are starting different courses for high school level to degree level and even at post graduate level through distance learning. This paper describes the best-known different machine learning techniques to boost up the e-Learning education standard and model. Comprehensively supervised and unsupervised techniques are described here for the e-Learning paradigm to auto reply of students' questions. Web bot is incorporated for the learning of students who are taking courses by remote mode. Due to the number of students enrolled in a particular course, student teacher interaction is a major challenge. Thus the solution is to train the Web based bot and make it available for the students' interaction 24/7. Main drawback of e-Learning environment is not frequent replies of student queries which we are going to cover by using web bot. The key demand is to deal with learning techniques but not fully automated. Training of the machine is performed on training data and validation is performed on test data set. Proposed idea of using the web bot in e-Learning is helpful to increase the learning curve of the students.
{"title":"Automated Reply to Students' Queries in E-Learning Environment Using Web-BOT","authors":"Muhammad Farhan, M. Iqbal, M. Aslam, A. Enríquez, A. Farooq, Saad Tanveer, A. P. Mejia","doi":"10.1109/MICAI.2012.18","DOIUrl":"https://doi.org/10.1109/MICAI.2012.18","url":null,"abstract":"Electronic Learning (e-Learning) is used to educate people in these days. Using e-Learning, a number of world ranking universities are starting different courses for high school level to degree level and even at post graduate level through distance learning. This paper describes the best-known different machine learning techniques to boost up the e-Learning education standard and model. Comprehensively supervised and unsupervised techniques are described here for the e-Learning paradigm to auto reply of students' questions. Web bot is incorporated for the learning of students who are taking courses by remote mode. Due to the number of students enrolled in a particular course, student teacher interaction is a major challenge. Thus the solution is to train the Web based bot and make it available for the students' interaction 24/7. Main drawback of e-Learning environment is not frequent replies of student queries which we are going to cover by using web bot. The key demand is to deal with learning techniques but not fully automated. Training of the machine is performed on training data and validation is performed on test data set. Proposed idea of using the web bot in e-Learning is helpful to increase the learning curve of the students.","PeriodicalId":348369,"journal":{"name":"2012 11th Mexican International Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129467918","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}
We present a Recognizing Textual Entailment(RTE) system based on different similarity metrics. The metrics used are string-based metrics and the Semantic Edit Distance Metric, which is proposed in this paper to address limitations of known semantic-based metrics and to support the decisions made by a simple method based on lexical similarity metrics.We add the scores of the metrics as features for a machine learning algorithm. The performance of our system is comparable with the average performance of the Recognizing Textual Entailment Challenges, though lower than that of the state-of-the-art methods.
{"title":"Recognizing Textual Entailment with a Semantic Edit Distance Metric","authors":"Miguel Rios, Alexander Gelbukh","doi":"10.1109/MICAI.2012.29","DOIUrl":"https://doi.org/10.1109/MICAI.2012.29","url":null,"abstract":"We present a Recognizing Textual Entailment(RTE) system based on different similarity metrics. The metrics used are string-based metrics and the Semantic Edit Distance Metric, which is proposed in this paper to address limitations of known semantic-based metrics and to support the decisions made by a simple method based on lexical similarity metrics.We add the scores of the metrics as features for a machine learning algorithm. The performance of our system is comparable with the average performance of the Recognizing Textual Entailment Challenges, though lower than that of the state-of-the-art methods.","PeriodicalId":348369,"journal":{"name":"2012 11th Mexican International Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344823","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：