A New Method of Improving BERT for Text Classification

2019 ◽  
pp. 442-452
Author(s):  
Shaomin Zheng ◽  
Meng Yang
Keyword(s):  
Text Classification ◽  
New Method

scholarly journals A New Vector Representation of Short Texts for Classification

2019 ◽  
Vol 17 (2) ◽  
pp. 241-249
Author(s):  
Yangyang Li ◽  
Bo Liu
Keyword(s):  
Text Classification ◽  
Web Search ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Classification Performance ◽  
New Method ◽  
Data Sets ◽  
Text Data ◽  
Short Text ◽  
Space Model

Short and sparse characteristics and synonyms and homonyms are main obstacles for short-text classification. In recent years, research on short-text classification has focused on expanding short texts but has barely guaranteed the validity of expanded words. This study proposes a new method to weaken these effects without external knowledge. The proposed method analyses short texts by using the topic model based on Latent Dirichlet Allocation (LDA), represents each short text by using a vector space model and presents a new method to adjust the vector of short texts. In the experiments, two open short-text data sets composed of google news and web search snippets are utilised to evaluate the classification performance and prove the effectiveness of our method.

A new method for multi-oriented graphics-scene-3D text classification in video

2016 ◽  
Vol 49 ◽  
pp. 19-42 ◽  
Author(s):  
Jiamin Xu ◽  
Palaiahnakote Shivakumara ◽  
Tong Lu ◽  
Chew Lim Tan ◽  
Seiichi Uchida
Keyword(s):  
Text Classification ◽  
New Method

Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model

2017 ◽  
Vol 8 (3) ◽  
pp. 610 ◽  
Author(s):  
M. Ali Fauzi ◽  
Agus Zainal Arifin ◽  
Sonny Christiano Gosaria
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Information Filtering ◽  
Feature Selection Method ◽  
Feature Space ◽  
Online News ◽  
New Method ◽  
Second Phase ◽  
Two Phase

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.

scholarly journals A Proposed Similarity Measure for Text-Classification

2020 ◽  
Vol 9 (4) ◽  
pp. 2232-2235
Keyword(s):  
Similarity Measure ◽  
Text Classification ◽  
Text Processing ◽  
Processing Method ◽  
New Method ◽  
Classification Method ◽  
Main Process ◽  
Display Performance

A Similarity measure is a main process in the text processing method. we have proposed a new method of similarity measure which improved the performance of the K-NN method. The proposed measure extended the accuracy of the text classification method. we have implemented proposed method with Amazon dataset and we observed the effectiveness of proposed similarity measure is increase the accuracy of the similarity between set of documents in a corpus. The end result display performance achieved by way of the proposed measure is higher than that obtained by others.

Text Classification Combined an Improved CHI and Category Relevance Factor

2012 ◽  
Vol 524-527 ◽  
pp. 3866-3869
Author(s):  
Pei Ying Zhang
Keyword(s):  
Natural Language ◽  
Text Classification ◽  
Classification System ◽  
Experimental Results ◽  
New Method ◽  
Main Concern ◽  
Important Improvement ◽  
Feature Weight ◽  
Series Of Experiments

Text classification is the task of assigning natural language textual documents to predefined categories based on their context. The main concern in this paper is to improve the accuracy of text classification system combined an improved CHI method and category relevance factor. Firstly, use an improved CHI method to select features from the raw features aim to reduce the dimensions of the features. Secondly, through the TF-CRF method to calculate the feature weight, this method mainly consider that the features have different distributions in different categories. Finally, we carried out a series of experiments compared with other methods using the F1-measure. Experimental results show that our new method makes an important improvement in all categories.

Chinese Text Classification with a KNN Classifier Using an Adjusted Feature Weighting Method

2011 ◽  
Vol 50-51 ◽  
pp. 700-703 ◽  
Author(s):  
Wei Ran Lin ◽  
Zhi Hui Wu ◽  
Li Chao Feng ◽  
Wai Bin Huang
Keyword(s):  
Chinese Text ◽  
Text Classification ◽  
Feature Weighting ◽  
New Method ◽  
Experimental Result ◽  
Weighting Method ◽  
Chinese Text Classification ◽  
Knn Classifier

KNN algorithm is used for Chinese text classification in this paper. First, TF-IDF is chosen as the feature weighting method. To the characteristics of corpus used in this paper, TF-IDF is adjusted to a new method. At last, experimental result shows the accuracy of KNN text classifier can be improved with the adjusted feature weighting method.

Hierarchical Text Classification Based on LDA and Domain Ontology

2013 ◽  
Vol 411-414 ◽  
pp. 1112-1116 ◽  
Author(s):  
Wei An ◽  
Qi Hua Liu
Keyword(s):  
Text Classification ◽  
Recall Rate ◽  
Domain Ontology ◽  
Experimental Results ◽  
New Method ◽  
Accuracy Rate ◽  
High Recall ◽  
High Recall Rate ◽  
Hierarchical Text Classification

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

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