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"Text classification based on machine learning for Tibetan social netwo" by Hui Lv, Fenfang Li et al.

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and text Convolutional Neural Networks (CNN) are used as classifiers to determine the best approach in Tibetan Social Network. In addition, term frequency-inverse document frequency (TF-IDF) is used to extract hot words and generate the word cloud. The results show that the random forest is significantly better than other machine learning algorithms on Tibetan text classification. ....

Tibetan Social Network , Convolutional Neural Networks , Naive Bayesian , Random Forest , Support Vector Machine , Tibetan Social , Data Mining , Social Network , Text Classification ,

ChatGPT: Potential Use Cases

ChatGPT: Potential Use Cases by Dr Rabi Padhy -
ChatGPT is a NLP (natural language processing) model chatbot developed by OpenAI based on the GPT-3 family of large ....

Pre Trained Transformer , Reinforcement Learning , Human Feedback , Generating Text , Dialogue Generation , Language Translation , Text Summarization , Text Classification , Question Answering , Text Completion ,

ML.NET 2.0 enhances text classification

Upgrade to Microsoft’s machine learning framework for .NET improves model building for text classification, introduces a sentence similarity API, and adds more AutoML capabilities. ....

Microsoft Research , Model Builder , Text Classification ,

"Optimising Automatic Text Classification Approach in Adaptive Online C" by Ya feng Zheng, Zhang hao Gao et al.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The Bi-LSTM is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the v ....

Adaptation Models , Attention Mechanism , Deep Learning , Feature Extraction , Long Short Term Memory Network , Nline Collaborative Discussion , Task Analysis , Ext Categorization , Text Classification ,

Microsoft previews text classification API for ML.NET

New text classification API for Microsoft’s open source machine learning framework streamlines model training by using data to fine-tune an existing model. ....

Microsoft Research , Bidirectional Encoder Representations , Text Classification , Visual Studio ,