https://github.com/UKPLab/linspector
공식 코드LINSPECTOR: Multilingual Probing Tasks for Word Representations
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/UKPLab/linspector-web
공식 코드LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations
https://github.com/VinAIResearch/BERTweet
공식 코드https://github.com/VinAIResearch/PhoBERT
공식 코드https://github.com/VinAIResearch/PhoNER_COVID19
공식 코드https://github.com/VinAIResearch/PhoNLP
공식 코드https://github.com/WHUIR/ME-CNER
공식 코드Exploiting Multiple Embeddings for Chinese Named Entity Recognition
https://github.com/WangJiuniu/ASTRAL
공식 코드ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
https://github.com/XiPotatonium/mproto
공식 코드https://github.com/XiPotatonium/pnr
공식 코드Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
https://github.com/Yarkona/TOF
공식 코드Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition
https://github.com/Yinghao-Li/CHMM-ALT
공식 코드BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
https://github.com/Yinghao-Li/Sparse-CHMM
공식 코드Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
https://github.com/YoumiMa/TablERT
공식 코드https://github.com/YuweiYin/CROP
공식 코드https://github.com/ZhixiuYe/HSCRF-pytorch
공식 코드https://github.com/ZihanWangKi/CrossWeigh
공식 코드CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
https://github.com/aakhundov/sequence-labeling
공식 코드https://github.com/abhyudaynj/LSTM-CRF-models
공식 코드Structured prediction models for RNN based sequence labeling in clinical text
https://github.com/abteen/alignment
공식 코드Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models
https://github.com/acadTags/Rare-disease-identification
공식 코드Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
https://github.com/achen-qaq/proml
공식 코드https://github.com/adlnlp/finllms
공식 코드https://github.com/adlnlp/mc-dre
공식 코드MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
https://github.com/adlnlp/trig_ner
공식 코드TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition
https://github.com/afshinrahimi/mmner
공식 코드https://github.com/ai-forever/mgpt
공식 코드https://github.com/ai-ku/wvec
공식 코드Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
https://github.com/ai-systems/transportability
공식 코드https://github.com/ai4finance-foundation/fingpt
공식 코드FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
https://github.com/aiforsec/cyner
공식 코드CyNER: A Python Library for Cybersecurity Named Entity Recognition
https://github.com/aiola-lab/whisper-ner
공식 코드WhisperNER: Unified Open Named Entity and Speech Recognition
https://github.com/airobotzhang/scdl
공식 코드Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning
https://github.com/aitslab/easyner
공식 코드https://github.com/akeepers/lear
공식 코드Enhanced Language Representation with Label Knowledge for Span Extraction
https://github.com/aksh555/cl-neril
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.