https://github.com/speedcell4/parserker
공식 코드To be Continuous, or to be Discrete, Those are Bits of Questions
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/sreyan88/aclm
공식 코드ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
https://github.com/stanfordnlp/en-worldwide-newswire
공식 코드Do "English" Named Entity Recognizers Work Well on Global Englishes?
https://github.com/stanfordnlp/stanza
공식 코드Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/statsmaths/cleanNLP
공식 코드A Tidy Data Model for Natural Language Processing using cleanNLP
https://github.com/stefan-it/historic-domain-adaptation-icdar
공식 코드Data Centric Domain Adaptation for Historical Text with OCR Errors
https://github.com/stefan-it/historic-ner
공식 코드https://github.com/stefanheng/proggen
공식 코드https://github.com/strawberryx/CoFEE
공식 코드https://github.com/studio-ousia/luke
공식 코드LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/suamin/t2ner
공식 코드Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts
https://github.com/successar/segment_influence_functions
공식 코드https://github.com/sue991/cofiner
공식 코드Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
https://github.com/supritivijay/nerda-con
공식 코드https://github.com/suralk/multiner
공식 코드A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
https://github.com/sustcsonglin/pointer-net-for-nested
공식 코드Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks
https://github.com/swapniljadhav1921/bert_crf_topic_detection
공식 코드Detecting Potential Topics In News Using BERT, CRF and Wikipedia
https://github.com/syuoni/eznlp
공식 코드https://github.com/sz128/few_shot_slot_tagging_and_NER
공식 코드Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
https://github.com/table-interpretation/wiki_table_ner
공식 코드Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
https://github.com/tadeephuy/vimq
공식 코드ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development
https://github.com/taisti/tasteset
공식 코드TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark
https://github.com/tangxuemei1995/chisiec
공식 코드CHisIEC: An Information Extraction Corpus for Ancient Chinese History
https://github.com/tareknaous/camel
공식 코드Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
https://github.com/tavo91/NER-WNUT17
공식 코드A Multi-task Approach for Named Entity Recognition in Social Media Data
https://github.com/teffland/ner-expected-entity-ratio
공식 코드Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss
https://github.com/tejasvaidhyadev/NER_Lab_Protocols
공식 코드Domain specific BERT representation for Named Entity Recognition of lab protocol
https://github.com/terenceau2/e-ner-dataset
공식 코드E-NER -- An Annotated Named Entity Recognition Corpus of Legal Text
https://github.com/thespectrewithin/cross-lingual_NER
공식 코드Neural Cross-Lingual Named Entity Recognition with Minimal Resources
https://github.com/thespectrewithin/joint-align
공식 코드Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
https://github.com/thudm/scholar-profiling
공식 코드https://github.com/thunlp/Few-NERD
공식 코드https://github.com/thunlp/pl-marker
공식 코드https://github.com/tian14267/ls_mbert
공식 코드Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer
https://github.com/tjunlp-lab/CogAlign
공식 코드CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
https://github.com/tobideusser/kpi-edgar
공식 코드KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.