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dc.contributor.authorChakraborty, Manisha
dc.date.accessioned2019-11-05T10:08:48Z
dc.date.available2019-11-05T10:08:48Z
dc.date.issued2019-11-05
dc.identifier.urihttp://dspace.uiu.ac.bd/handle/52243/1500
dc.description.abstractDocument categorisation is a quintessential example of a natural language processing quest which includes sorting documents by their content into one or more predefined classes. This thesis proposes a model which consists of multilayer Dense Neural Network with Term Frequency - Inverse Document Frequency (TF-IDF) as feature selection technique in terms of Bangla text document categorisation. This proposed system is divided into three consecutive steps: i) preprocessing raw text data and extracting feature using TF- IDF, ii) designing the model architecture and fitting the model to training set, and iii) evaluating model performance on test set by measuring accuracy and weighted average of F1-score. It is observed from experiments that the proposed method exhibits higher accuracy (85.208%) and weighted F1 score (0.85) compared to the other well-known classification algorithms (K Nearest Neighbor, Decision Tree, Support Vector Machine, Stochastic Gradient Descent, Multinomial Na¨ıve Bayes, and Logistic Regression) for Bangla text document classification.en_US
dc.language.isoen_USen_US
dc.publisherUnited International Universityen_US
dc.subjectDocument categorisationen_US
dc.subjectnatural language processingen_US
dc.subjectDense Neural Networken_US
dc.subjectBangla text document classificationen_US
dc.titleBangla Document Categorisation using Multilayer Dense Neural Network with TF-IDFen_US
dc.typeThesisen_US


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