419 papers with code • 2 benchmarks • 9 datasets

The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...


Use these libraries to find NER models and implementations
4 papers
2 papers
2 papers
See all 6 libraries.

Most implemented papers

Neural Architectures for Named Entity Recognition

glample/tagger NAACL 2016

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.

Unsupervised Cross-lingual Representation Learning at Scale

facebookresearch/XLM ACL 2020

We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale.

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

guillaumegenthial/sequence_tagging ACL 2016

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.

Bidirectional LSTM-CRF Models for Sequence Tagging

determined22/zh-ner-tf 9 Aug 2015

It can also use sentence level tag information thanks to a CRF layer.

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.

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.

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.

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.

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.

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.