Text Classification

Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics. Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others. In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems. ( Image credit: Text Classification Algorithms: A Survey )

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Content

Universal Language Model Fine-tuning for Text Classification

Semi-supervised Sequence Learning

Bag of Tricks for Efficient Text Classification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

FastText.zip: Compressing text classification models

Character-level Convolutional Networks for Text Classification

Distributed Representations of Sentences and Documents

Revisiting Semi-Supervised Learning with Graph Embeddings

Very Deep Convolutional Networks for Text Classification