NER
578 papers with code • 6 benchmarks • 24 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...
Benchmarks
These leaderboards are used to track progress in NER
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Libraries
Use these libraries to find NER models and implementationsDatasets
MNIST
CoNLL 2003
Most implemented papers
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
btaille/sincere
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EMNLP 2020
Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.
On-the-Job Learning with Bayesian Decision Theory
Our goal is to deploy a high-accuracy system starting with zero training examples.
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
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
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
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Deep Active Learning for Named Entity Recognition
tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
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WS 2017
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition
We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese.
A Deep Neural Network Model for the Task of Named Entity Recognition
ZhixiuYe/NER-pytorch
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International Journal of Machine Learning and Computing, Vol. 9, No. 1 2018
One of the most important factors which directly and significantly affects the quality of the neural sequence labeling is the selection and encoding the input features to generate rich semantic and grammatical representation vectors.
