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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rug-compling/hmm-reps
공식 코드https://github.com/rz-zhang/SeqMix
공식 코드SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
https://github.com/s-t-full-text-knowledge-mining/ssci-bert
공식 코드SsciBERT: A Pre-trained Language Model for Social Science Texts
https://github.com/s1998/progressivetraincodeswitch
공식 코드https://github.com/sEhsanTaher/Beheshti-NER
공식 코드https://github.com/sagorbrur/bnlp
공식 코드BNLP: Natural language processing toolkit for Bengali language
https://github.com/sahilkhose/SMM4H21
공식 코드BERT based Transformers lead the way in Extraction of Health Information from Social Media
https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/salt-nlp/robustdemo
공식 코드Robustness of Demonstration-based Learning Under Limited Data Scenario
https://github.com/samanjoy2/gazz-ban-ner
공식 코드https://github.com/sanmusunrise/ARNs
공식 코드Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
https://github.com/sberbank-ai-lab/RuMedBench
공식 코드RuMedBench: A Russian Medical Language Understanding Benchmark
https://github.com/shangjingbo1226/AutoNER
공식 코드Learning Named Entity Tagger using Domain-Specific Dictionary
https://github.com/shannonai/knn-ner
공식 코드$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search
https://github.com/shashank140195/raredis
공식 코드https://github.com/shengwei-tian/dependency-parser-visualization
공식 코드AutoLLM-CARD: Towards a Description and Landscape of Large Language Models
https://github.com/shijie-wu/crosslingual-nlp
공식 코드Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
https://github.com/shmulvad/zero-for-ner
공식 코드Zero-Shot Learning in Named-Entity Recognition with External Knowledge
https://github.com/shoumikchow/statement-network-NeurIPS2018
공식 코드Statement networks: a power structure narrative as depicted by newspapers
https://github.com/shreyashub/BioFLAIR
공식 코드BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks
https://github.com/shuaiwa16/OtherClassNER
공식 코드Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
https://github.com/shubham30vatsal/web-of-law
공식 코드Classification of US Supreme Court Cases using BERT-Based Techniques
https://github.com/shubhamkumarnigam/legaleval23_nonet
공식 코드Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation
https://github.com/shuhengl/acl2023_conllpp
공식 코드https://github.com/shuhewang1998/gnn-sl
공식 코드https://github.com/shuhewang1998/gpt-ner
공식 코드https://github.com/shulk97/daniel
공식 코드https://github.com/sinovation/ZEN
공식 코드ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
https://github.com/skydustz/aec-domain-corpora
공식 코드Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain
https://github.com/sntcristian/zibaldoner
공식 코드Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone
https://github.com/social-machines/tweebanknlp
공식 코드https://github.com/solkx/toe
공식 코드https://github.com/som-shahlab/trove
공식 코드Ontology-driven weak supervision for clinical entity classification in electronic health records
https://github.com/sonvx/word2vecVN
공식 코드https://github.com/source-data/soda-data
공식 코드Integrating curation into scientific publishing to train AI models
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