Natural Language Processing
 
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Named Entity Recognition (NER)

891 papers with code • 76 benchmarks • 122 datasets

Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

Mark Watney visited Mars
B-PER I-PER O B-LOC

( Image credit: Zalando )

Libraries

Use these libraries to find Named Entity Recognition (NER) models and implementations
flairNLP/flair
6 papers
13,581
huggingface/transformers
5 papers
125,385
l3cube-pune/MarathiNLP
4 papers
87
dmlc/gluon-nlp
3 papers
2,549
See all 7 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

SciBERT: A Pretrained Language Model for Scientific Text

allenai/scibert • • IJCNLP 2019

Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.

5
Paper
Code

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

stanfordnlp/stanza • • ACL 2020

We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.

5
Paper
Code

Biomedical and Clinical English Model Packages in the Stanza Python NLP Library

stanfordnlp/stanza • • 29 Jul 2020

We introduce biomedical and clinical English model packages for the Stanza Python NLP library.

5
Paper
Code

CrossNER: Evaluating Cross-Domain Named Entity Recognition

zliucr/CrossNER • • 8 Dec 2020

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.

5
Paper
Code

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

zliucr/CrossNER • • 12 Jun 2003

We describe the CoNLL-2003 shared task: language-independent named entity recognition.

4
Paper
Code

GloVe: Global Vectors for Word Representation

stanfordnlp/GloVe • EMNLP 2014

4
Paper
Code

An Incremental Parser for Abstract Meaning Representation

mdtux89/amr-evaluation • EACL 2017

We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.

4
Paper
Code

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

iesl/dilated-cnn-ner • • EMNLP 2017

Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.

4
Paper
Code

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

kimiyoung/transfer • 18 Mar 2017

Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks.

4
Paper
Code

Few-shot Learning for Named Entity Recognition in Medical Text

mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL • • 13 Nov 2018

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).

4
Paper
Code
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