https://github.com/pefimov/cross-lingual-adjustment
공식 코드The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer
Papers with Code 아카이브
Named Entity Recognition (NER)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 439
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/pfliu-nlp/Named-Entity-Recognition-NER-Papers
공식 코드Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
https://github.com/phenixace/MFE-NER
공식 코드MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
https://github.com/phucty/MTab
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/phucty/mtab_tool
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/pkunlp-icler/censor
공식 코드https://github.com/pkunlp-icler/scl-rai
공식 코드https://github.com/plasmashen/BackdoorPTM
공식 코드https://github.com/pluslabnlp/clap
공식 코드Contextual Label Projection for Cross-Lingual Structured Prediction
https://github.com/pmulcaire/rosita
공식 코드Polyglot Contextual Representations Improve Crosslingual Transfer
https://github.com/prashantksharma/CaCD
공식 코드https://github.com/primerai/primer-research
공식 코드BabyBear: Cheap inference triage for expensive language models
https://github.com/princeton-nlp/PURE
공식 코드A Frustratingly Easy Approach for Entity and Relation Extraction
https://github.com/princeton-nlp/datamux
공식 코드https://github.com/psunlpgroup/container
공식 코드CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
https://github.com/qingyu-qc/bioner_gerbera
공식 코드Augmenting Biomedical Named Entity Recognition with General-domain Resources
https://github.com/qiuhaolu/tner
공식 코드Large Language Models Struggle in Token-Level Clinical Named Entity Recognition
https://github.com/qznan/spankl
공식 코드A Neural Span-Based Continual Named Entity Recognition Model
https://github.com/racai-ai/romanian-distilbert
공식 코드Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
https://github.com/raja-kumar/knowledge-grounded-tods
공식 코드Task Oriented Conversational Modelling With Subjective Knowledge
https://github.com/ramisa2108/bangla-complex-named-entity-recognition-challenge
공식 코드BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition
https://github.com/randyzhouran/conner
공식 코드ConNER: Consistency Training for Cross-lingual Named Entity Recognition
https://github.com/randyzhouran/melm
공식 코드MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
https://github.com/raotnameh/End-to-end-E2E-Named-Entity-Recognition-from-English-Speech
공식 코드https://github.com/rasmuskaer/mdapt_supplements
공식 코드MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
https://github.com/ratmcu/wikipiifed
공식 코드https://github.com/realsarm/reportql
공식 코드https://github.com/rendanim/mphayaner
공식 코드https://github.com/renll/sparselt
공식 코드https://github.com/revdotcom/speech-datasets/tree/main/earnings21
공식 코드https://github.com/ritual-uh/da_ner
공식 코드Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
https://github.com/ritual-uh/style_ner
공식 코드https://github.com/rsabilio/nereval-braziliancorporatetranscriptions
공식 코드https://github.com/rsabilio/nereval-braziliancorporatetranscripts
공식 코드https://github.com/rtmaww/EntLM
공식 코드https://github.com/rtmaww/x-piece
공식 코드Searching for Optimal Subword Tokenization in Cross-domain NER
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.