https://github.com/SUSTechBruce/G-MAP
공식 코드G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Determine the extent of the events in a text.
Other names: Event Tagging; Event Identification
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
Exploiting Multiple Embeddings for Chinese Named Entity Recognition
ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
CyNER: A Python Library for Cybersecurity Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.