Results 11 to 20 of about 6,040,759 (312)
International Journal of Applied Sciences and Smart Technologies, 2021 By and large, we don't know to talk and read the territorial dialects that are spoken in our nation. So we have accepted Tamil language as it is our territorial and numerous doesn't get it. In our task, the content in Tamil language is stacked from Wikipedia.
Omprakash Yadav +4 more
openaire +4 more sources
A Survey on Text Classification Algorithms: From Text to Predictions
Information, 2022 In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the ...
A. Gasparetto +3 more
semanticscholar +3 more sources
Sensors, 2022 As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis.
Meikang Chen +4 more
doaj +1 more source
Text Classification Based on Graph Neural Networks and Dependency Parsing [PDF]
Jisuanji kexue, 2022 Text classification is a basic and important task in natural language processing.It is widely used in language processing scenarios such as news classification,topic tagging and sentiment analysis.The current text classification models generally do not ...
YANG Xu-hua, JIN Xin, TAO Jin, MAO Jian-fei
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Graph Convolutional Networks for Text Classification [PDF]
AAAI Conference on Artificial Intelligence, 2018 Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification.
Liang Yao, Chengsheng Mao, Yuan Luo
semanticscholar +1 more source
AEDA: An Easier Data Augmentation Technique for Text Classification [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text.
Akbar Karimi, L. Rossi, A. Prati
semanticscholar +1 more source
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment [PDF]
AAAI Conference on Artificial Intelligence, 2019 Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models
Di Jin +3 more
semanticscholar +1 more source
Noisy Channel Language Model Prompting for Few-Shot Text Classification [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the ...
Sewon Min +3 more
semanticscholar +1 more source
Research on cross-language text intelligence classification method based on deep learning [PDF]
Zhihui kongzhi yu fangzhen, 2023 Text intelligence classification is the basic work in the field of intelligence analysis. At present, text intelligence classification work is usually oriented to a single language, and there are relatively few studies on cross-language text intelligence
YIN Lai-xiang, LI Zhi-qiang, LI Yuan-long
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Graph neural networks for text classification: a survey [PDF]
Artificial Intelligence Review, 2023 Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with ...
Kunze Wang, Yihao Ding, S. Han
semanticscholar +1 more source