named-entity-recognition
778 papers with code • 2 benchmarks • 1 datasets
Benchmarks
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Libraries
Use these libraries to find named-entity-recognition models and implementationsMost implemented papers
Beheshti-NER: Persian Named Entity Recognition Using BERT
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
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
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
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
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
Our goal is to deploy a high-accuracy system starting with zero training examples.
Harnessing Deep Neural Networks with Logic Rules
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
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.