https://github.com/flairNLP/flair
공식 코드HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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...
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
Comparing and combining some popular NER approaches on Biomedical tasks
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
A Comparative Analysis of Static Word Embeddings for Hungarian
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
RISC: Generating Realistic Synthetic Bilingual Insurance Contract
FiNER-ORD: Financial Named Entity Recognition Open Research Dataset
FiNER-ORD: Financial Named Entity Recognition Open Research Dataset
KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission
Generating Medical Prescriptions with Conditional Transformer
Synthetic4Health: Generating Annotated Synthetic Clinical Letters
Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction
A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.