737 papers with code • 2 benchmarks • 0 datasets

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

Most implemented papers

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

PaddlePaddle/PaddleNLP 31 Aug 2019

The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.

CrossNER: Evaluating Cross-Domain Named Entity Recognition

zliucr/CrossNER 8 Dec 2020

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.

Empower Sequence Labeling with Task-Aware Neural Language Model

LiyuanLucasLiu/LM-LSTM-CRF 13 Sep 2017

In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.

LINSPECTOR: Multilingual Probing Tasks for Word Representations

UKPLab/linspector CL 2020

We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.

Advancing NLP with Cognitive Language Processing Signals

DS3Lab/zuco-nlp 4 Apr 2019

Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.

Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

yahshibu/nested-ner-tacl2020-transformers 5 Sep 2019

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.

Entity, Relation, and Event Extraction with Contextualized Span Representations

dwadden/dygiepp IJCNLP 2019

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.

CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese

CLUEbenchmark/CLUENER2020 13 Jan 2020

In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.

AraBERT: Transformer-based Model for Arabic Language Understanding

aub-mind/araBERT LREC 2020

Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus.

Parallel sequence tagging for concept recognition

OntoGene/craft-st 16 Mar 2020

In all 20 annotation sets of the concept-annotation task, our system outperforms the pipeline system reported as a baseline in the CRAFT shared task 2019.