https://github.com/lxucs/multilingual-sl
공식 코드Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
Papers with Code 아카이브
Named Entity Recognition (NER)
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- 평가표
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- 보존된 평가 기록
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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/lyutyuh/asp
공식 코드https://github.com/m3rg-repo/MatSciBERT
공식 코드MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction
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
https://github.com/marekrei/sequence-labeler
공식 코드https://github.com/markus-eberts/spert
공식 코드Span-based Joint Entity and Relation Extraction with Transformer Pre-training
https://github.com/markustyj/fsponer_ecai2024
공식 코드FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
https://github.com/masakhane-io/masakhane-ner
공식 코드https://github.com/masakhane-io/masakhane-ner/tree/main/MasakhaNER2.0
공식 코드MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
https://github.com/masoud-monajati/llm_bio_ner
공식 코드LLMs in Biomedicine: A study on clinical Named Entity Recognition
https://github.com/matejklemen/morphological-comment-filtering
공식 코드Enhancing deep neural networks with morphological information
https://github.com/matejklemen/morphological-dependency-parsing
공식 코드Enhancing deep neural networks with morphological information
https://github.com/mckysse/gain
공식 코드https://github.com/mckysse/msd
공식 코드https://github.com/mdtux89/amr-evaluation
공식 코드https://github.com/mebzmoren/cebuaner
공식 코드CebuaNER: A New Baseline Cebuano Named Entity Recognition Model
https://github.com/mehdibenamorr/Token-Positional-Bias
공식 코드Technical Report: Impact of Position Bias on Language Models in Token Classification
https://github.com/mehdibenamorr/bert-positional-bias-ner
공식 코드Technical Report: Impact of Position Bias on Language Models in Token Classification
https://github.com/melelbgu/sage
공식 코드https://github.com/mengf1/PAL
공식 코드Learning how to Active Learn: A Deep Reinforcement Learning Approach
https://github.com/mhtang1995/bopn
공식 코드A Boundary Offset Prediction Network for Named Entity Recognition
https://github.com/michiyasunaga/LinkBERT
공식 코드https://github.com/michiyasunaga/pos_adv
공식 코드Robust Multilingual Part-of-Speech Tagging via Adversarial Training
https://github.com/microsoft/DeBERTa
공식 코드https://github.com/microsoft/MetaXL
공식 코드MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
https://github.com/microsoft/Unicoder
공식 코드XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
https://github.com/microsoft/binder
공식 코드Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
https://github.com/microsoft/genalog
공식 코드Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
https://github.com/microsoft/scglab
공식 코드https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/vert-papers
공식 코드Decomposed Meta-Learning for Few-Shot Named Entity Recognition
https://github.com/microsoft/vert-papers/tree/master/papers/AdvPicker
공식 코드AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
https://github.com/microsoft/vert-papers/tree/master/papers/CAN-NER
공식 코드CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
https://github.com/microsoft/vert-papers/tree/master/papers/GRN-NER
공식 코드GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.