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- W4387250532 abstract "Topic modelling has changed the way to do text mining. It is a technique to discover topics and make clusters of similar words in large volume of documents. After analyzing almost 3000 medium articles on topic modeling, an extensive survey on topic modelling has been presented in this paper. It includes Topic modelling methods, different evolution models of latent Dirichlet allocation (LDA) and its utilization in different areas of technology. The LDA operate and assess documents in the form of multinomial distribution. Similarly, every topic is assessed in the form of multinomial distribution over words. LDA can calculate and analyze the steadily increasing words in a particular corpus and can be utilized as a good resemblance of natural text. Essays are most useful ways for checking knowledge, but manual grading might miss out on some minor details and lead to errors in result generation. Therefore, we believe that automatic assessment system is an optimal solution to tackle this issue. We propose using Topic Modelling usage for the assessment system. Our research paper is an attempt to investigate whether automatic assessment systems can detect similar information without guessing meaning of sentences based on in structurally different texts, the way examiner does without guessing meaning of sentences based on word or need to classify entire document based on topic before evaluation. Earlier research on these methods show that machine matches four out of five human findings which also gives room for improvement." @default.
- W4387250532 created "2023-10-03" @default.
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- W4387250532 date "2023-07-29" @default.
- W4387250532 modified "2023-10-06" @default.
- W4387250532 title "Automatic Assessment System Using Topic Modelling and TF-IDF Approach" @default.
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- W4387250532 doi "https://doi.org/10.1109/aic57670.2023.10263898" @default.
- W4387250532 hasPublicationYear "2023" @default.
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