Papers with Code 아카이브
Relation Extraction
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Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
Source: Deep Residual Learning for Weakly-Supervised Relation Extraction
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AndrewSukhobok95/DL_GraphEntity_project
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
https://github.com/Anery/RSAN
https://github.com/BITCS-Information-Retrieval-2020/search-rattailcollagen1
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
https://github.com/Beacontownfc/paddle_luke_stable
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/BordiaS/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/Cartus/AGGCN
Attention Guided Graph Convolutional Networks for Relation Extraction
https://github.com/DavidMortensen/Bachelor-readinglist
https://github.com/DeepGraphLearning/FewShotRE
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
https://github.com/DevSinghSachan/ssl_text_classification
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
https://github.com/EmilyAlsentzer/clinicalBERT
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Hannibal046/GridTST
https://github.com/HazyResearch/bootleg
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
https://github.com/IAmHedgehog/SagDRE
https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5
https://github.com/IMvision12/keras-vision-models
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/INK-USC/REGD
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
https://github.com/INK-USC/procedural-extraction
Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis
https://github.com/JiachengLi1995/UCTopic
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/JiyangZhang/learning-to-reweight-in-relation-extraion
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
https://github.com/Jize-W/HingeABL
https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/10.3.Clinical_RE_SparkNLP_Paper_Reproduce.ipynb
Deeper Clinical Document Understanding Using Relation Extraction
https://github.com/KLUE-benchmark/KLUE-baseline
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KeLi-gavin/CS8750
https://github.com/LitianD/Joint_RE
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
https://github.com/MS-P3/code3/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/ManasRMohanty/DS5500-capstone
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/MeRajat/SolvingAlmostAnythingWithBert
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/Meet1995/Named-Entity-Recognition-And-Relationship-Linking
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
https://github.com/Mihaw99/CNNRE
https://github.com/Mind23-2/MindCode-136
ERNIE: Enhanced Language Representation with Informative Entities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.