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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 126–150 of 1729 papers

TitleStatusHype
Trustworthy AI: Securing Sensitive Data in Large Language Models—0
Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPT—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
AutoLLM-CARD: Towards a Description and Landscape of Large Language ModelsCode0
Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical DocumentsCode0
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
SLIMER-IT: Zero-Shot NER on Italian LanguageCode0
NER-Luxury: Named entity recognition for the fashion and luxury domain—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models—0
Cross-Lingual News Event Correlation for Stock Market Trend Prediction—0
LLM-DER:A Named Entity Recognition Method Based on Large Language Models for Chinese Coal Chemical Domain—0
A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration—0
Model Selection Through Model Sorting—0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Synthetic4Health: Generating Annotated Synthetic Clinical LettersCode0
WhisperNER: Unified Open Named Entity and Speech RecognitionCode3
Towards identifying Source credibility on Information Leakage in Digital Gadget Market—0
Preserving Empirical Probabilities in BERT for Small-sample Clinical Entity Recognition—0
LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition—0
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language ModelsCode1
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study—0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
Investigating LLM Applications in E-Commerce—0
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction—0
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