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Text Classification Algorithms: A Survey [PDF]
Information, 2019 In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications.
Barnes, Laura E. +5 more
core +4 more sources
Bag of Tricks for Efficient Text Classification [PDF]
Conference of the European Chapter of the Association for Computational Linguistics, 2016 This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training
Bojanowski, Piotr +3 more
core +2 more sources
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tuning has remarkable superiority in the low-data scenario over the generic ...
Shengding Hu +5 more
semanticscholar +1 more source
Text Classification via Large Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classification.
Xiaofei Sun +6 more
semanticscholar +1 more source
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference [PDF]
Conference of the European Chapter of the Association for Computational Linguistics, 2020 Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with “task descriptions” in natural language (e.g., Radford et al., 2019). While this approach underperforms its supervised counterpart, we show in this
Timo Schick, Hinrich Schütze
semanticscholar +1 more source
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy. Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy ...
Zihan Wang +4 more
semanticscholar +1 more source
BertGCN: Transductive Text Classification by Combining GNN and BERT [PDF]
Findings, 2021 In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the dataset and represents documents as nodes using BERT representations.
Yuxiao Lin +6 more
semanticscholar +1 more source
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment.
Zhuoyan Li +3 more
semanticscholar +1 more source
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion.
Jason Wei, Kai Zou
semanticscholar +1 more source
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.
Zhihao Wen, Yuan Fang
semanticscholar +1 more source