Text Classification

987 papers with code • 150 benchmarks • 131 datasets

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 )


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Most implemented papers

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

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Semi-supervised Sequence Learning

tensorflow/models NeurIPS 2015

In our experiments, we find that long short term memory recurrent networks after being pretrained with the two approaches are more stable and generalize better.

Universal Language Model Fine-tuning for Text Classification

fastai/fastai ACL 2018

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.

Bag of Tricks for Efficient Text Classification

facebookresearch/fastText EACL 2017

This paper explores a simple and efficient baseline for text classification.

RoBERTa: A Robustly Optimized BERT Pretraining Approach

pytorch/fairseq 26 Jul 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

FastText.zip: Compressing text classification models

facebookresearch/fastText 12 Dec 2016

We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.

Character-level Convolutional Networks for Text Classification

makcedward/nlpaug NeurIPS 2015

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.

Distributed Representations of Sentences and Documents

inejc/paragraph-vectors 16 May 2014

Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models.

Revisiting Semi-Supervised Learning with Graph Embeddings

tkipf/gcn 29 Mar 2016

We present a semi-supervised learning framework based on graph embeddings.

Very Deep Convolutional Networks for Text Classification

dongjun-Lee/text-classification-models-tf EACL 2017

The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks.