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Named Entity Recognition (NER)

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Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

Mark	Watney	visited	Mars
B-PER	I-PER	O	B-LOC

<span style="color:grey; opacity: 0.6">( Image credit: Zalando )</span>

논문

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Named Entity Recognition (NER)

논문

Flippi: End To End GenAI Assistant for E-Commerce

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models

Improving Named Entity Transcription with Contextual LLM-based Revision

Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering

Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering

EL4NER: Ensemble Learning for Named Entity Recognition via Multiple Small-Parameter Large Language Models

Label-Guided In-Context Learning for Named Entity Recognition

Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone

FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)

RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models

Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?

Nested Named Entity Recognition as Single-Pass Sequence Labeling

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations

Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques

LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs

A Comparative Analysis of Static Word Embeddings for Hungarian

Development of a WAZOBIA-Named Entity Recognition System

Symbol-based entity marker highlighting for enhanced text mining in materials science with generative AI

Logits-Constrained Framework with RoBERTa for Ancient Chinese NER

Positional Attention for Efficient BERT-Based Named Entity Recognition

VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language

Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM

Natural Language Processing tools for Pharmaceutical Manufacturing Information Extraction from Patents

Methods for Recognizing Nested Terms

LLMs as Data Annotators: How Close Are We to Human Performance

EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition

Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justice

Keyword Extraction, and Aspect Classification in Sinhala, English, and Code-Mixed Content

Federated Learning with Layer Skipping: Efficient Training of Large Language Models for Healthcare NLP

Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation

DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition

myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging

Unmasking the Reality of PII Masking Models: Performance Gaps and the Call for Accountability

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition

Scraping the Shadows: Deep Learning Breakthroughs in Dark Web Intelligence