https://github.com/Meet1995/Named-Entity-Recognition-And-Relationship-Linking
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Papers with Code 아카이브
Relation Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 8
- 보존된 평가 기록
- 33
Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mind23-2/MindCode-136
ERNIE: Enhanced Language Representation with Informative Entities
https://github.com/MindSpore-scientific-2/code-4/tree/main/pytorch_convgru
Convolutional Gated Recurrent Units for Medical Relation Classification
https://github.com/NEUNLP-RE/Entity-aware-RC
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/roformer
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/ProKil/FewRel
https://github.com/Shujian2015/meta-embedding-paper-list
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
https://github.com/Soikonomou/albert_final
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/albert_final_infer12
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/albert_final_infer8
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/bert_new
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/bert_new_new
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/UnknownGenie/altered-BERT-KPE
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/Valdegg/anlp_rbert
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/amazon-science/tanl
Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amore-upf/masked-coreference
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/anlausch/LIBERT
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
https://github.com/avramandrei/BERT-Sequence-Labeling
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
https://github.com/avramandrei/UPB-at-SemEval-2020-Task-6-Pretrained-Language-Models-for-DefinitionExtraction
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
https://github.com/chielingyueh/anaphora_resolution_chemical_patents
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/chippybautista/zuco-sentiment-analysis
https://github.com/congyingxia/IncrementalFSTC
https://github.com/context-mover/HypEval
Experiments with Three Approaches to Recognizing Lexical Entailment
https://github.com/cypressd1999/FYP_2021
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/declare-lab/hyperred
https://github.com/dfki-nlp/mtb-bert-em
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/diffbot/knowledge-net
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/genggui001/fine_tuning_bert_for_joint_entity_and_relation_extraction_in_chinese_medical_text
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
https://github.com/hieudepchai/BERT_IE
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/hieumdt/SCS-EERE
https://github.com/huggingface/transformers
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/jpablou/Matching-The-Blanks-Ths
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/jwallat/knowledge-probing
https://github.com/lavis-nlp/spert
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
https://github.com/levubk/AEPA
https://github.com/mandarjoshi90/coref
SpanBERT: Improving Pre-training by Representing and Predicting Spans
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.