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/nguyennth/joint-ib-models
공식 코드Span-based Named Entity Recognition by Generating and Compressing Information
https://github.com/nguyenvulebinh/vietnamese-roberta
공식 코드https://github.com/niclasgriesshaber/gemini_historical_dataset_pipeline
공식 코드Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents
https://github.com/niclasgriesshaber/llm_historical_dataset_benchmarking
공식 코드Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents
https://github.com/nicolamelluso/SkillNER
공식 코드https://github.com/nicpopovic/ember
공식 코드https://github.com/nicpopovic/stoke
공식 코드https://github.com/nimameghdadi/uottawa-at-legallens-2024-transformer-based-classification
공식 코드uOttawa at LegalLens-2024: Transformer-based Classification Experiments
https://github.com/ningkko/umls_colloquial
공식 코드Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
https://github.com/ningkko/umls_colloquialism
공식 코드Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
https://github.com/nlp-uoregon/trankit
공식 코드Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
https://github.com/nlpaueb/greek-bert
공식 코드https://github.com/nlpie-research/compact-biomedical-transformers
공식 코드https://github.com/nlpie-research/lightweight-clinical-transformers
공식 코드Lightweight Transformers for Clinical Natural Language Processing
https://github.com/nlpwm-whu/tadner
공식 코드Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
https://github.com/npoe/covid-qa
공식 코드https://github.com/ntunlp/Zero-Shot-Cross-Lingual-NER
공식 코드https://github.com/ntunlp/kmlm
공식 코드Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
https://github.com/ntunlp/multisense_embedding_alignment
공식 코드Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
https://github.com/nusnlp/contextemb-wsd
공식 코드Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
https://github.com/oagarwal/entity-switched-ner
공식 코드https://github.com/oagarwal/temporal-effects
공식 코드Temporal Effects on Pre-trained Models for Language Processing Tasks
https://github.com/olivettigroup/materials-synthesis-generative-models
공식 코드Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
https://github.com/omni-us/research-dataset-sGMB
공식 코드A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
https://github.com/omni-us/research-e2e-pagereader
공식 코드A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
https://github.com/onurgu/joint-ner-and-md-tagger
공식 코드Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
https://github.com/open-mmlab/mmocr
공식 코드MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
https://github.com/opennlg/openba-v2
공식 코드OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
https://github.com/osainz59/Ask2Transformers
공식 코드Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
https://github.com/osu-nlp-group/bio-tokenization
공식 코드Biomedical Language Models are Robust to Sub-optimal Tokenization
https://github.com/ottowg/gsap-ner
공식 코드https://github.com/pablorom2004/insight-buddy-ai-app
공식 코드https://github.com/paramansh/propaganda_detection
공식 코드https://github.com/pavanbaswani/fincausal_sharedtask-2023
공식 코드https://github.com/pavel-blinov/RuMedBench
공식 코드RuMedBench: A Russian Medical Language Understanding Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.