text-classification

668 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

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.

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.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

google-research/text-to-text-transfer-transformer arXiv 2019

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

Character-level Convolutional Networks for Text Classification

gaussic/text-classification-cnn-rnn 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.

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.

Revisiting Semi-Supervised Learning with Graph Embeddings

tkipf/gcn 29 Mar 2016

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

Unsupervised Data Augmentation for Consistency Training

google-research/uda NeurIPS 2020

In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.