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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/cslydia/BiFlaG
공식 코드Bipartite Flat-Graph Network for Nested Named Entity Recognition
https://github.com/cslydia/Hire-NER
공식 코드Hierarchical Contextualized Representation for Named Entity Recognition
https://github.com/csu-signal/xlingual-robustness
공식 코드Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
https://github.com/cuhksz-nlp/AESINER
공식 코드Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
https://github.com/cuhksz-nlp/SANER
공식 코드Named Entity Recognition for Social Media Texts with Semantic Augmentation
https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs
공식 코드Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
https://github.com/dair-iitd/zgul
공식 코드https://github.com/daixiangau/acl2020-transition-discontinuous-ner
공식 코드https://github.com/daixiangau/naacl2019-select-pretraining-data-for-ner
공식 코드Using Similarity Measures to Select Pretraining Data for NER
https://github.com/daiyongya/markbert
공식 코드https://github.com/dalab/end2end_neural_el
공식 코드https://github.com/damo-nlp-sg/contproto
공식 코드https://github.com/damo-nlp-sg/pmr
공식 코드https://github.com/dave-s477/somesci_code
공식 코드https://github.com/davidandym/multilingual-NER
공식 코드Sources of Transfer in Multilingual Named Entity Recognition
https://github.com/dbmdz/berts
공식 코드https://github.com/dbmdz/clef-hipe
공식 코드hmBERT: Historical Multilingual Language Models for Named Entity Recognition
https://github.com/dccuchile/lightweight-spanish-language-models
공식 코드https://github.com/declare-lab/vip
공식 코드Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding
https://github.com/deezer/music-ner-eacl2023
공식 코드https://github.com/deezer/net-ner-probing
공식 코드Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
https://github.com/dfki-nlp/fewie
공식 코드A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
https://github.com/dfki-nlp/mobie
공식 코드https://github.com/dhfbk/kind
공식 코드KIND: an Italian Multi-Domain Dataset for Named Entity Recognition
https://github.com/dialogue-evaluation/runne
공식 코드https://github.com/dice-group/BENGAL
공식 코드BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking
https://github.com/didi/delta
공식 코드https://github.com/diego-alvarado/nlp_for_pharma_manufacturing
공식 코드https://github.com/ding-zj/glode
공식 코드https://github.com/ding-zj/sam
공식 코드https://github.com/dinghanshen/SWEM
공식 코드Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
https://github.com/diqiuzhuanzhuan/semeval-2023
공식 코드https://github.com/dki-lab/few-shot-bioie
공식 코드Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
https://github.com/dkucuk/Tweet-Dataset-NER-SD
공식 코드A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
https://github.com/dlt-science/ESG-DLT-LitReview
공식 코드Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature
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