Document Classification

194 papers with code • 19 benchmarks • 14 datasets

Document Classification is a procedure of assigning one or more labels to a document from a predetermined set of labels.

Source: Long-length Legal Document Classification


Use these libraries to find Document Classification models and implementations

Most implemented papers

Graph Attention Networks

PetarV-/GAT ICLR 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Semi-Supervised Classification with Graph Convolutional Networks

dmlc/dgl 9 Sep 2016

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.

Revisiting Semi-Supervised Learning with Graph Embeddings

tkipf/gcn 29 Mar 2016

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

On Calibration of Modern Neural Networks

gpleiss/temperature_scaling ICML 2017

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

facebookresearch/LASER TACL 2019

We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.

Improving Language Understanding by Generative Pre-Training

huggingface/transformers Preprint 2018

We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations

sinovation/ZEN Findings of the Association for Computational Linguistics 2020

Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

hazyresearch/flash-attention 27 May 2022

We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method.

SPECTER: Document-level Representation Learning using Citation-informed Transformers

allenai/specter ACL 2020

We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.

Geometric deep learning on graphs and manifolds using mixture model CNNs

dmlc/dgl CVPR 2017

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.