Text classification using NLP based machine learning approach
“Online learning is not the next big thing; it is the new big thing.” “eLearning is changing. And we will see new models, new technologies, and designs emerge. So, let us drop the “e” – or at least give it a new and wider definition.” These quotes influenced us to build this paper. During this unpre...
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Sprache: | eng |
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Zusammenfassung: | “Online learning is not the next big thing; it is the new big thing.” “eLearning is changing. And we will see new models, new technologies, and designs emerge. So, let us drop the “e” – or at least give it a new and wider definition.” These quotes influenced us to build this paper. During this unprecedented time, we all are using online resources. Our paper will enhance the teaching experience of the students. Especially school-going kids, it is the duty of teacher, influencer, and faculty to enhance the teaching experience so that, one can learn at their ease without any hustle. Our paper aims at building a website and android application (in the coming days) which will identify the individual words of any sentences entered. It will parse the string entered, remove the unwanted characters, and pass the remaining string to our AI model. As a result, the AI model will return the parts of speech the individual word belongs to. We have always seen students struggling to find that a given word is either a noun, or an adverb, or a preposition, and so on. We have searched the web but could not find any relevant websites doing the same task. There is a gigantic search engine on which we can search individual words but again, for sentences, people struggle. This may sound very simple for AI/ML enthusiasts but building a model and deploying that on a website and android application is not a cup of tea. Tech giants like Google, Facebook, and Microsoft are building different AI models and deploying them successfully on various platforms like Website, Android Application, iOS. However, we have done it successfully. |
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ISSN: | 0094-243X 1551-7616 |
DOI: | 10.1063/5.0080301 |