https://github.com/akshittyagi/intclass
공식 코드Fast Intent Classification for Spoken Language Understanding
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/alankarj/cross_lingual_ner
공식 코드Entity Projection via Machine Translation for Cross-Lingual NER
https://github.com/aleksficek/covidnews-ner
공식 코드How to tackle an emerging topic? Combining strong and weak labels for Covid news NER
https://github.com/alibaba-nlp/aishell-ner
공식 코드https://github.com/alibaba-nlp/kb-ner
공식 코드ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
https://github.com/alirezasalemi7/SemEval2021-Toxic-Spans-Detection
공식 코드https://github.com/allanj/ner_with_dependency
공식 코드https://github.com/allegro/klejbenchmark-baselines
공식 코드KLEJ: Comprehensive Benchmark for Polish Language Understanding
https://github.com/allenai/beacon
공식 코드On-the-fly Definition Augmentation of LLMs for Biomedical NER
https://github.com/allenai/scibert
공식 코드https://github.com/altsoph/baer
공식 코드Actionable Entities Recognition Benchmark for Interactive Fiction
https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amazon-science/e2e-docie
공식 코드REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
https://github.com/amirsartipi13/translated-english-benchmarks-to-persian
공식 코드https://github.com/amitpandey-research/complex_ner
공식 코드Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER
https://github.com/amzn/amazon-weak-ner-needle
공식 코드Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
https://github.com/anastasia-zhukova/anea
공식 코드ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts
https://github.com/andreaskuster/uw-nlp
공식 코드https://github.com/andrewzamai/slimer_it
공식 코드https://github.com/annedirkson/breakingbert
공식 코드https://github.com/anzeyimana/kinyabert-acl2022
공식 코드https://github.com/applicaai/ner-resources
공식 코드https://github.com/ariepratama/syntax-driven-da
공식 코드Syntax-driven Data Augmentation for Named Entity Recognition
https://github.com/arijitthegame/BERT-CNN-models
공식 코드https://github.com/arijrb/multilingual-auxiliary-tasks-training-bridging-the-gap-between-languages-for-zero-shot-transfer-of-
공식 코드https://github.com/arjun7m/aa_ner_fr
공식 코드https://github.com/asadifar/HCqa
공식 코드HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph
https://github.com/asahi417/tner
공식 코드T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
https://github.com/asappresearch/slue-toolkit
공식 코드SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
https://github.com/asappresearch/spoken-ner
공식 코드On the Use of External Data for Spoken Named Entity Recognition
https://github.com/asappresearch/structshot
공식 코드Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
https://github.com/asappresearch/wav2seq
공식 코드Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
https://github.com/astrazeneca/kazu
공식 코드Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
https://github.com/aub-mind/araBERT
공식 코드AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/aws-health-ai/multi_domain_lm
공식 코드An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
https://github.com/ayoubhammal/ckss4ner
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.