named-entity-recognition
737 papers with code • 2 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in named-entity-recognition
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Libraries
Use these libraries to find named-entity-recognition models and implementationsMost implemented papers
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
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
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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
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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
We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.
Advancing NLP with Cognitive Language Processing Signals
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
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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
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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
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
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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
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.
