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NER

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...

Papers

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Showing 901–925 of 1729 papers

TitleStatusHype
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Home Appliance Review Research Via Adversarial Reptile—0
Nested Named Entity Recognition for Chinese Electronic Health Records with QA-based Sequence Labeling—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Actionable Entities Recognition Benchmark for Interactive FictionCode1
Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning—0
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual KnowledgeCode1
Paradigm Shift in Natural Language ProcessingCode1
GERNERMED -- An Open German Medical NER ModelCode1
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial AttackCode0
Named Entity Recognition and Classification on Historical Documents: A SurveyCode1
reproducing "ner and pos when nothing is capitalized"Code0
The futility of STILTs for the classification of lexical borrowings in Spanish—0
MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity RecognitionCode0
Context-NER : Contextual Phrase Generation at ScaleCode1
Low-Resource Named Entity Recognition Based on Multi-hop Dependency TriggerCode0
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer EncodersCode0
Evaluating Transferability of BERT Models on Uralic LanguagesCode0
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition ModelsCode1
Clinical Trial Information Extraction with BERT—0
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework—0
AdaK-NER: An Adaptive Top-K Approach for Named Entity Recognition with Incomplete Annotations—0
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-TrainingCode1
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction—0
Truth Discovery in Sequence Labels from CrowdsCode0
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