Text Classification of Kannada Webpages Using Various Pre-processing Agents

2014 ◽  
pp. 235-243 ◽  
Author(s):  
N. Deepamala ◽  
P. Ramakanth Kumar
Keyword(s):  
Text Classification

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1411-1418
Author(s):  
Ravi Kauthale
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Support Systems ◽  
The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

Fault Text Classification of Rotating Machine Based BERT

2021 ◽  
Author(s):  
Chen Ling ◽  
Liu Yimin ◽  
Ji Lianlian
Keyword(s):  
Text Classification ◽  
Rotating Machine

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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