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/santhoshmani888/Aspect-Based-sentiment-analysis
Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture
https://github.com/sberbank-ai/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/scofield7419/DiaRE-D2G
https://github.com/sebastianarnold/TeXoo
https://github.com/shaoxiongji/awesome-knowledge-graph
https://github.com/shimorina/relation-extraction-db-wikidata
WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivammehta25/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shkarupa-alex/tfclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shmulvad/zero-for-ner
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/shunk031/simple-aesthetics-predictor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sidsvash26/iterx
Iterative Document-level Information Extraction via Imitation Learning
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/sincerass/mvlpt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sithu31296/simple-object-tracking
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sktwtrg/stanza_ja_test
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/slczgwh/REDN
Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
https://github.com/stanford-oval/genienlp
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/stevekgyang/sccl
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
https://github.com/stonybrooknlp/musique
https://github.com/svjan5/gnns-for-nlp
Neural Graph Embedding Methods for Natural Language Processing
https://github.com/synlp/RE-DMP
https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/taohan10200/weather-5k
https://github.com/taoqiongxing/Enhancing-Relation-Extraction-using-Syntactic-Indicators-and-Sentential-Contexts
Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
https://github.com/tetsu9923/scireviewgen
https://github.com/textflint/textflint
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/thibaultdouzon/long-range-document-transformer
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/thunlp/gp-gnn
Graph Neural Networks with Generated Parameters for Relation Extraction
https://github.com/tomaarsen/spanmarkerner
https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
https://github.com/towhee-io/towhee
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/tticoin/LSTM-ER
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
https://github.com/tutujingyugang1/ChatVLA_public
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/ucinlp/null-prompts
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.