Results 31 to 40 of about 1,761,138 (274)
Graph Convolutional Networks for Text Classification
, 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.
Luo, Yuan, Mao, Chengsheng, Yao, Liang
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Explicit Interaction Model towards Text Classification
, 2018 Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models,
Chin, Zhaozheng +5 more
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Research on manufacturing text classification based on improved genetic algorithm
Brazilian Archives of Biology and TechnologyAccording to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category.
Zhou Kaijun, Tong Yifei
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Text Classification For Authorship Attribution Analysis
, 2013 Authorship attribution mainly deals with undecided authorship of literary texts. Authorship attribution is useful in resolving issues like uncertain authorship, recognize authorship of unknown texts, spot plagiarism so on. Statistical methods can be used
Elayidom, M. Sudheep +3 more
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Social Media Text Classification by Enhancing Well-Formed Text Trained Model
Journal of ICT Research and Applications, 2016 Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove.
Phat Jotikabukkana +3 more
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Generative Multi-Task Learning for Text Classification
IEEE Access, 2020 Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which ...
Wei Zhao, Hui Gao, Shuhui Chen, Nan Wang
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Text Classification: A Sequential Reading Approach
, 2011 We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information ...
Denoyer, Ludovic +2 more
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How to Fine-Tune BERT for Text Classification?
, 2020 Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in ...
Huang, Xuanjing +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Arteriovenous malformations (AVMs) are rare, high‐flow, vascular anomalies that can occur either sporadically or as part of a genetic syndrome. AVMs can progress with serious morbidity and even mortality if left unchecked. Sirolimus is an mTOR inhibitor that is effective in low‐flow vascular malformations; however, its role in AVMs is unclear.
Will Swansson +3 more
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Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text
International Journal of Crowd ScienceDuring a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy.
Zhichao Wang +3 more
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