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/jiyfeng/drlm
공식 코드A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
https://github.com/jjyunlp/stad
공식 코드STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
https://github.com/johngiorgi/seq2rel
공식 코드A sequence-to-sequence approach for document-level relation extraction
https://github.com/jonghokimsnu/temporal-reasoning-network
공식 코드https://github.com/julianneuberger/pet-data-augmentation
공식 코드Leveraging Data Augmentation for Process Information Extraction
https://github.com/justinphan3110/SciFive
공식 코드SciFive: a text-to-text transformer model for biomedical literature
https://github.com/jyf123/CP-GCN
공식 코드Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
https://github.com/jzbjyb/SpanRel
공식 코드Generalizing Natural Language Analysis through Span-relation Representations
https://github.com/jzhang38/lpd
공식 코드Better Few-Shot Relation Extraction with Label Prompt Dropout
https://github.com/kentchang/subversive-characters-chr2024
공식 코드https://github.com/kevinsrr/repal
공식 코드Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
https://github.com/khaitran22/vaediff-docre
공식 코드VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
https://github.com/klimzaporojets/DWIE
공식 코드DWIE: an entity-centric dataset for multi-task document-level information extraction
https://github.com/klimzaporojets/e2e-kb-ie
공식 코드https://github.com/kmeng01/rome
공식 코드https://github.com/knodle/knodle
공식 코드https://github.com/knowledge-computing/geolm
공식 코드GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
https://github.com/komeijiforce/metaie
공식 코드MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
https://github.com/kosciukiewicz/similarity_based_memory_re
공식 코드Similarity-based Memory Enhanced Joint Entity and Relation Extraction
https://github.com/kracr/cross-doc-relation-extraction
공식 코드https://github.com/kracr/document-level-relation-extraction
공식 코드Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
https://github.com/kracr/reonto-relation-extraction
공식 코드ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
https://github.com/kwanhui/finrelextract
공식 코드Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech
https://github.com/kwj0x7f/care
공식 코드CARE: Co-Attention Network for Joint Entity and Relation Extraction
https://github.com/laituan245/bio_relex
공식 코드Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
공식 코드https://github.com/lanwuwei/GigaBERT
공식 코드An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
https://github.com/lasigeBioTM/BiONT
공식 코드BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
https://github.com/lasigeBioTM/PGR
공식 코드https://github.com/lavis-nlp/jerex
공식 코드An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
https://github.com/leeworry/MTD4SemiRE/blob/main/README.md
공식 코드From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction
https://github.com/lehgtrung/ASPER
공식 코드ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction
https://github.com/lfoppiano/matsci-lumen
공식 코드https://github.com/lhrlab/text2nkg
공식 코드Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
https://github.com/liaad/tieval
공식 코드tieval: An Evaluation Framework for Temporal Information Extraction Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.