scholarly journals Deep learning based multi-label text classification of UNGA resolutions

2020 ◽  
Author(s):  
Francesco Sovrano ◽  
Monica Palmirani ◽  
Fabio Vitali
Keyword(s):  
Deep Learning ◽  
Text Classification

scholarly journals A systematic review of text classification research based on deep learning models in Arabic language

2020 ◽  
Vol 10 (6) ◽  
pp. 6629
Author(s):  
Ahlam Wahdan ◽  
Sendeyah AL Hantoobi ◽  
Said A. Salloum ◽  
Khaled Shaalan
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Neural Networks ◽  
Deep Learning ◽  
Text Classification ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Learning Techniques

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

scholarly journals Technical Domain Classification of Bangla Text using BERT

2021 ◽  
Author(s):  
Koyel Ghosh ◽  
Apurbalal Senapati
Keyword(s):  
Deep Learning ◽  
Computer Science ◽  
Communication Technology ◽  
Text Classification ◽  
Language Model ◽  
Learning Model ◽  
Coarse Grained ◽  
Deep Learning Model

Coarse-grained tasks are primarily based on Text classification, one of the earliest problems in NLP, and these tasks are done on document and sentence levels. Here, our goal is to identify the technical domain of a given Bangla text. In Coarse-grained technical domain classification, such a piece of the Bangla text provides information about specific Coarse-grained technical domains like Biochemistry (bioche), Communication Technology (com-tech), Computer Science (cse), Management (mgmt), Physics (phy) Etc. This paper uses a recent deep learning model called the Bangla Bidirectional Encoder Representations Transformers (Bangla BERT) mechanism to identify the domain of a given text. Bangla BERT (Bangla-Bert-Base) is a pretrained language model of the Bangla language. Later, we discuss the Bangla BERT accuracy and compare it with other models that solve the same problem.

Automated text classification of near-misses from safety reports: An improved deep learning approach

2020 ◽  
Vol 44 ◽  
pp. 101060 ◽  
Author(s):  
Weili Fang ◽  
Hanbin Luo ◽  
Shuangjie Xu ◽  
Peter E.D. Love ◽  
Zhenchuan Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Learning Approach ◽  
Near Misses ◽  
Automated Text Classification

scholarly journals A Deep Learning-Based Text Classification of Adverse Nursing Events

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenjing Lu ◽  
Wei Jiang ◽  
Na Zhang ◽  
Feng Xue
Keyword(s):  
Deep Learning ◽  
Health System ◽  
Medical Care ◽  
Text Classification ◽  
Rapid Development ◽  
Classification Model ◽  
Diagnosis And Treatment ◽  
Event Analysis ◽  
Classification Methods

Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient’s diagnosis and treatment results and even increase the patient’s pain and burden. Additionally, It is high likely to cause accidents and disputes and affect normal medical work and personnel safety and is not conducive to the development of the health system. Due to the rapid development of modern medicine, health and safety of patients have become the most concerned issue in society and patient safety is an important part of medical care management. Research and events have shown that classified management of adverse nursing events, event analysis, and improvement measures are beneficial, specifically to the health system, to continuously improve the quality of medical care and reduce the occurrence of adverse nursing events. In the management of adverse nursing events, it is very important to categorize the text reports of adverse nursing events and divide these into different categories and levels. Traditional reports of adverse nursing events are mostly unstructured and simple data, often relying on manual classification, which is difficult to analyze. Furthermore, data is relatively inaccurate and practical reference significance is not obvious. In this paper, we have extensively evaluated various deep learning-based classification methods which are specifically designed for the healthcare systems. It becomes possible with the development of science and technology; text classification methods based on deep learning are gradually entering people’s field of vision. Additionally, we have proposed a text classification model for adverse nursing events in the health system. Experiments and data comparison test of both the proposed deep learning-based method and existing methods in the text classification of nursing adverse events effect are performed. These results show the exceptional performance of the proposed mechanism in terms of various evaluation metrics.

scholarly journals Convolutional neural network-based model for web-based text classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5785
Author(s):  
Satyabrata Aich ◽  
Sabyasachi Chakraborty ◽  
Hee-Cheol Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Learning Models ◽  
Real World Problem ◽  
Traditional Approaches ◽  
The Web

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different topics at different levels, which may adversely affect the accuracy of traditional approaches. With the invention of deep learning models, which already have the capability of providing good accuracy in the field of image processing and speech recognition, the problems inherent in the traditional text classification model can be overcome. Currently, there are several deep learning models such as a convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory that are widely used for various text-related tasks; however, among them, the CNN model is popular because it is simple to use and has high accuracy for text classification. In this study, classification of random texts on the web into categories is attempted using a CNN-based model by changing the hyperparameters and sequence of text vectors. We attempt to tune every hyperparameter that is unique for the classification task along with the sequences of word vectors to obtain the desired accuracy; the accuracy is found to be in the range of 85–92%. This model can be considered as a reliable model and applied to solve real-world problem or extract useful information for various text mining applications.</p></td></tr></tbody></table>

scholarly journals Applications of Deep Learning in News Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Menghan Zhang
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Recall Rate ◽  
Technological Advancement ◽  
Text Data ◽  
Healthcare Data ◽  
Feature Information ◽  
And Performance ◽  
The Relationship

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.

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.

Sign in / Sign up

Export Citation Format

Share Document