Natural Language Processing
 
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Named Entity Recognition (NER)

886 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
13,561
huggingface/transformers
5 papers
124,793
l3cube-pune/MarathiNLP
4 papers
87
dmlc/gluon-nlp
3 papers
2,548
See all 7 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

A Unified MRC Framework for Named Entity Recognition

ShannonAI/mrc-for-flat-nested-ner • • ACL 2020

Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.

8
Paper
Code

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

studio-ousia/luke • • EMNLP 2020

In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.

8
Paper
Code

TENER: Adapting Transformer Encoder for Named Entity Recognition

fastnlp/TENER • • 10 Nov 2019

The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.

7
Paper
Code

Few-NERD: A Few-Shot Named Entity Recognition Dataset

thunlp/Few-NERD • • ACL 2021

In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.

7
Paper
Code

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

UKPLab/emnlp2017-bilstm-cnn-crf • • 21 Jul 2017

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.

6
Paper
Code

CamemBERT: a Tasty French Language Model

huggingface/transformers • • ACL 2020

We show that the use of web crawled data is preferable to the use of Wikipedia data.

6
Paper
Code

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

UKPLab/emnlp2017-bilstm-cnn-crf • • EMNLP 2017

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.

5
Paper
Code

The Natural Language Decathlon: Multitask Learning as Question Answering

salesforce/decaNLP • • ICLR 2019

Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.

5
Paper
Code

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

luanyi/DyGIE • • EMNLP 2018

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.

5
Paper
Code

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

roomylee/entity-aware-relation-classification • • 23 Jan 2019

Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.

5
Paper
Code
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