named-entity-recognition
892 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find named-entity-recognition models and implementations
NuNER
Most 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.
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.
Dice Loss for Data-imbalanced NLP Tasks
ShannonAI/dice_loss_for_NLP
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ACL 2020
Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
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.
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.
