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NER
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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...
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/katzurik/neretrieve
공식 코드NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval
https://github.com/kawatami/dner
공식 코드https://github.com/keenon/lense
공식 코드https://github.com/klimzaporojets/DWIE
공식 코드DWIE: an entity-centric dataset for multi-task document-level information extraction
https://github.com/knowledgeresearch/kaner
공식 코드Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
https://github.com/komeijiforce/x-ner
공식 코드Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
https://github.com/konstantinjdobler/focus
공식 코드FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
https://github.com/kormilitzin/med7
공식 코드Med7: a transferable clinical natural language processing model for electronic health records
https://github.com/kwanhui/finrelextract
공식 코드Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech
https://github.com/l3cube-pune/MarathiNLP
공식 코드https://github.com/l3cube-pune/code-mixed-nlp
공식 코드L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models
https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
공식 코드https://github.com/lancopku/Multi-Order-LSTM
공식 코드Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
https://github.com/leduckhai/multimed
공식 코드https://github.com/legal-nlp-ekstep/legal_ner
공식 코드https://github.com/leon-bit-9527/ener
공식 코드E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
https://github.com/lepessoa/rita
공식 코드RITA: Automatic Framework for Designing of Resilient IoT Applications
https://github.com/lfoppiano/grobid-superconductors
공식 코드Automatic extraction of materials and properties from superconductors scientific literature
https://github.com/lfoppiano/matsci-lumen
공식 코드https://github.com/lgessler/microbert
공식 코드https://github.com/liatb282/metaxlr
공식 코드https://github.com/lihaoxin2020/narrowbert
공식 코드NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
https://github.com/liu-haoyue/ner-net
공식 코드https://github.com/liukun95/Noisy-NER-Confidence-Estimation
공식 코드https://github.com/liupeip-cs/iie4mner
공식 코드https://github.com/liyongqi2002/TadNER
공식 코드Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
https://github.com/liyp0095/CuPUL
공식 코드Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
https://github.com/ljvmiranda921/calamancy
공식 코드https://github.com/ljynlp/w2ner
공식 코드Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/louchao98/nner_as_parsing
공식 코드Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
https://github.com/luka-group/pasta
공식 코드https://github.com/lxucs/multilingual-sl
공식 코드Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
https://github.com/mainlp/auxtasks-bavarian-sid
공식 코드https://github.com/mainlp/barner
공식 코드Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
https://github.com/mainlp/ner-disagreements
공식 코드Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.