The Influence of Text Length on Text Classification Model

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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Enhancing the performance of cancer text classification model based on cancer hallmarks

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i2.pp316-323 ◽

2021 ◽

Vol 10 (2) ◽

pp. 316

Author(s):

Noha Ali ◽

Ahmed H. AbuEl-Atta ◽

Hala H. Zayed

Keyword(s):

Neural Network ◽

Language Processing ◽

Text Classification ◽

State Of The Art ◽

Classification Model ◽

Biomedical Text ◽

Cancer Hallmarks ◽

Embedding Technique ◽

Proposed Model ◽

Biomedical Text Classification

<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

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Friends Recommendation Based on KBERT-CNN Text Classification Model

10.1109/ijcnn52387.2021.9533618 ◽

2021 ◽

Author(s):

Ning Pan ◽

Wenbin Yao ◽

Xiaoyong Li

Keyword(s):

Text Classification ◽

Classification Model

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Improving Techniques for Naïve Bayes Text Classifiers

Handbook of Research on Text and Web Mining Technologies ◽

10.4018/978-1-59904-990-8.ch007 ◽

2010 ◽

pp. 111-127

Author(s):

Han-joon Kim

Keyword(s):

Text Classification ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Systems ◽

Classification Model ◽

Learning Approaches ◽

Learning Framework ◽

The Em Algorithm ◽

Meta Learning ◽

Text Classifiers

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.

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Chinese Text Classification Model Based on Deep Learning

Future Internet ◽

10.3390/fi10110113 ◽

2018 ◽

Vol 10 (11) ◽

pp. 113 ◽

Cited By ~ 17

Author(s):

Yue Li ◽

Xutao Wang ◽

Pengjian Xu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Language Processing ◽

Chinese Text ◽

Text Classification ◽

Short Term Memory ◽

Classification Model ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

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