https://github.com/thu-bpm/promu
공식 코드Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
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/thu-bpm/rapl
공식 코드RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
https://github.com/thu-bpm/re2
공식 코드Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
https://github.com/thu-keg/maven-argument
공식 코드https://github.com/thu-keg/maven-ere
공식 코드https://github.com/thu-keg/omnievent
공식 코드OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
https://github.com/thu-keg/sqc-score
공식 코드Evaluating Generative Language Models in Information Extraction as Subjective Question Correction
https://github.com/thudm/autore
공식 코드AutoRE: Document-Level Relation Extraction with Large Language Models
https://github.com/thuiar/CRL
공식 코드Consistent Representation Learning for Continual Relation Extraction
https://github.com/thunlp/CorefBERT
공식 코드Coreferential Reasoning Learning for Language Representation
https://github.com/thunlp/DIAG-NRE
공식 코드DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
https://github.com/thunlp/DSDocRE
공식 코드Denoising Relation Extraction from Document-level Distant Supervision
https://github.com/thunlp/DocRED
공식 코드DocRED: A Large-Scale Document-Level Relation Extraction Dataset
https://github.com/thunlp/ERICA
공식 코드https://github.com/thunlp/ERNIE
공식 코드ERNIE: Enhanced Language Representation with Informative Entities
https://github.com/thunlp/KernelGAT
공식 코드Coreferential Reasoning Learning for Language Representation
https://github.com/thunlp/Neural-Snowball
공식 코드https://github.com/thunlp/OpenKE
공식 코드https://github.com/thunlp/OpenNRE
공식 코드OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
https://github.com/thunlp/PTR
공식 코드https://github.com/thunlp/PathNRE
공식 코드https://github.com/thunlp/RE-Context-or-Names
공식 코드Learning from Context or Names? An Empirical Study on Neural Relation Extraction
https://github.com/thunlp/fewrel
공식 코드FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
https://github.com/thunlp/pl-marker
공식 코드https://github.com/tianran/vecdcs
공식 코드Learning Semantically and Additively Compositional Distributional Representations
https://github.com/tjunlp-lab/CogAlign
공식 코드CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
https://github.com/tmliang/CGRE
공식 코드Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
https://github.com/tobideusser/kpi-edgar
공식 코드KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
https://github.com/toneli/petailor-for-bio-triple-extraction
공식 코드BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
https://github.com/tonytan48/kd-docre
공식 코드Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
https://github.com/tonytan48/re-docred
공식 코드Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
https://github.com/tricktreat/trimf
공식 코드A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
https://github.com/truthless11/HRL-RE
공식 코드A Hierarchical Framework for Relation Extraction with Reinforcement Learning
https://github.com/ttthy/ure
공식 코드https://github.com/tuur/WLLETlinkClassification
공식 코드Word-Level Loss Extensions for Neural Temporal Relation Classification
https://github.com/u-alberta/nlpworkbench
공식 코드NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.