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NER
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The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ding-zj/sam
공식 코드https://github.com/diqiuzhuanzhuan/semeval-2023
공식 코드https://github.com/dlt-science/ESG-DLT-LitReview
공식 코드Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature
https://github.com/dmis-lab/bern2
공식 코드BERN2: an advanced neural biomedical named entity recognition and normalization tool
https://github.com/dmis-lab/bioner-generalization
공식 코드How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?
https://github.com/dmis-lab/conner
공식 코드Enhancing Label Consistency on Document-level Named Entity Recognition
https://github.com/dongguanting/msdp-fewshot-ner
공식 코드A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
https://github.com/dorringel/SWVM
공식 코드https://github.com/dpressel/mead-baseline
공식 코드Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
https://github.com/dreamyang-liu/SciAnnotate
공식 코드SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
https://github.com/drgriffis/NeuralVecmap
공식 코드https://github.com/ds4dh/GLiNER-biomed
공식 코드GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition
https://github.com/ds4dh/jnrf
공식 코드https://github.com/dsfsi/puoberta
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
https://github.com/dsfsi/puodata
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
https://github.com/dugu9sword/LEMON
공식 코드Chinese Named Entity Recognition Augmented with Lexicon Memory
https://github.com/edchengg/easyproject
공식 코드Frustratingly Easy Label Projection for Cross-lingual Transfer
https://github.com/edchengg/gollie-transfusion
공식 코드Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
https://github.com/elenamer/noisebench
공식 코드NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition
https://github.com/elenanereiss/Legal-Entity-Recognition
공식 코드A Dataset of German Legal Documents for Named Entity Recognition
https://github.com/emanuelaboros/ocr-sensitive-neurons
공식 코드Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents
https://github.com/emma1066/retrieval-augmented-it-openner
공식 코드Retrieval Augmented Instruction Tuning for Open NER with Large Language Models
https://github.com/emma1066/zero-shot-ner-with-chatgpt
공식 코드https://github.com/emorynlp/stem-cell-hypothesis
공식 코드The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders
https://github.com/emseoyk/verifiner
공식 코드VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
https://github.com/eoduself/udl
공식 코드Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval
https://github.com/eulring/dmner
공식 코드DMNER: Biomedical Entity Recognition by Detection and Matching
https://github.com/ewsheng/byteNER
공식 코드https://github.com/facebookresearch/Clinical-Trial-Parser
공식 코드Information Extraction of Clinical Trial Eligibility Criteria
https://github.com/fastnlp/fastHan
공식 코드https://github.com/fdschmidt93/free-lunch-xlt
공식 코드Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging
https://github.com/fdschmidt93/ofa-xlt
공식 코드https://github.com/fe1ixxu/mixed-gradient-few-shot
공식 코드https://github.com/fishjh2/merge_label
공식 코드Merge and Label: A novel neural network architecture for nested NER
https://github.com/fitz-like-coding/cwtm
공식 코드CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
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