named-entity-recognition

778 papers with code • 2 benchmarks • 1 datasets

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Libraries

Use these libraries to find named-entity-recognition models and implementations

Datasets


Most implemented papers

Beheshti-NER: Persian Named Entity Recognition Using BERT

sEhsanTaher/Beheshti-NER NSURL 2019

In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian.

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

cambridgeltl/xcopa EMNLP 2020

The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.

KLUE: Korean Language Understanding Evaluation

KLUE-benchmark/KLUE 20 May 2021

We introduce Korean Language Understanding Evaluation (KLUE) benchmark.

ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

idiap/atco2-corpus 8 Nov 2022

In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data.

Natural Language Processing (almost) from Scratch

faramarzmunshi/d2l-nlp 2 Mar 2011

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling.

On-the-Job Learning with Bayesian Decision Theory

keenon/lense NeurIPS 2015

Our goal is to deploy a high-accuracy system starting with zero training examples.

Harnessing Deep Neural Networks with Logic Rules

ZhitingHu/logicnn ACL 2016

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.

Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity

monikkinom/ner-lstm 31 Oct 2016

In this paper we describe an end to end Neural Model for Named Entity Recognition NER) which is based on Bi-Directional RNN-LSTM.

PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese

LIAAD/py-pampo 30 Dec 2016

This paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities.

Semi-supervised sequence tagging with bidirectional language models

flairNLP/flair ACL 2017

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.