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/lingyongyan/docds
공식 코드https://github.com/liuwei1206/connrel
공식 코드https://github.com/liuyeah/dsare
공식 코드https://github.com/ljynlp/w2ner
공식 코드Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/longjubai/eecs598_llm_relationextraction
공식 코드Graphical Reasoning: LLM-based Semi-Open Relation Extraction
https://github.com/longls777/emma
공식 코드https://github.com/luanyi/DyGIE
공식 코드A General Framework for Information Extraction using Dynamic Span Graphs
https://github.com/luka-group/NLI_as_Indirect_Supervision
공식 코드Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
https://github.com/luka-group/causal-view-of-entity-bias
공식 코드https://github.com/luka-group/mrcod
공식 코드Multi-hop Evidence Retrieval for Cross-document Relation Extraction
https://github.com/luka-group/sure
공식 코드Summarization as Indirect Supervision for Relation Extraction
https://github.com/luka-group/unist
공식 코드https://github.com/lukeming-tsinghua/remod
공식 코드Multimodal Learning on Graphs for Disease Relation Extraction
https://github.com/lumia-group/apl
공식 코드https://github.com/lumia-group/i2srm
공식 코드I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction
https://github.com/lumia-group/lace
공식 코드Document-Level Relation Extraction with Relation Correlation Enhancement
https://github.com/lylylylylyly/simplefsre
공식 코드A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
https://github.com/lyutyuh/asp
공식 코드https://github.com/m3rg-repo/MatSciBERT
공식 코드MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction
https://github.com/mainlp/crossre
공식 코드https://github.com/mainlp/discourse_probes
공식 코드Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set
https://github.com/mainlp/syntax-pre-training-for-re
공식 코드Silver Syntax Pre-training for Cross-Domain Relation Extraction
https://github.com/makisekuurisu/ecrim
공식 코드https://github.com/malllabiisc/ConfGCN
공식 코드Neural Graph Embedding Methods for Natural Language Processing
https://github.com/malllabiisc/RESIDE
공식 코드RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
https://github.com/mariosaenger/biore-kplm-benchmark
공식 코드Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction
https://github.com/markus-eberts/spert
공식 코드Span-based Joint Entity and Relation Extraction with Transformer Pre-training
https://github.com/maryambrj/alien
공식 코드Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
https://github.com/mashengkun/cpl
공식 코드Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors
https://github.com/mataney/BootstrappingRelationExtractors
공식 코드Bootstrapping Relation Extractors using Syntactic Search by Examples
https://github.com/matnlp/hiclre
공식 코드HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
https://github.com/megagonlabs/zett
공식 코드https://github.com/mgormley/pacaya
공식 코드Improved Relation Extraction with Feature-Rich Compositional Embedding Models
https://github.com/mhany90/Multi-WikiRE
공식 코드X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
https://github.com/michiyasunaga/LinkBERT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.