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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/nju-websoft/GLRE
공식 코드Global-to-Local Neural Networks for Document-Level Relation Extraction
https://github.com/nju-websoft/cear
공식 코드Improving Continual Relation Extraction by Distinguishing Analogous Semantics
https://github.com/nju-websoft/fan
공식 코드Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction
https://github.com/nju-websoft/kire
공식 코드Enhancing Document-level Relation Extraction by Entity Knowledge Injection
https://github.com/nju-websoft/sckd
공식 코드Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction
https://github.com/nlpgm/srvf
공식 코드Enhancing Relation Extraction via Supervised Rationale Verification and Feedback
https://github.com/nlpie-research/lightweight-clinical-transformers
공식 코드Lightweight Transformers for Clinical Natural Language Processing
https://github.com/nlpub/projlearn
공식 코드Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
https://github.com/ntunlp/kmlm
공식 코드Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
https://github.com/ntunlplab/lifeeventdialog
공식 코드https://github.com/nusnlp/MFA4RE
공식 코드https://github.com/nusnlp/MHRE
공식 코드A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
https://github.com/nusnlp/PtrNetDecoding4JERE
공식 코드Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
https://github.com/oceanypt/DR_RE
공식 코드Jointly Extracting Relations with Class Ties via Effective Deep Ranking
https://github.com/oeberle/task_gaze_transformers
공식 코드Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?
https://github.com/oezyurty/replm
공식 코드Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
https://github.com/ofirarviv/improving-ud
공식 코드On the Relation between Syntactic Divergence and Zero-Shot Performance
https://github.com/olivesgatech/cross_predict
공식 코드Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior
https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/osainz59/Ask2Transformers
공식 코드Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
https://github.com/osu-nlp-group/qa4re
공식 코드Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
https://github.com/pat-jj/genres
공식 코드GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
https://github.com/patriziobellan86/petsotaandchallenges
공식 코드https://github.com/patriziobellan86/processextractionfromtextsotaandchallenges
공식 코드https://github.com/patverga/bran
공식 코드Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
https://github.com/patverga/torch-relation-extraction
공식 코드Multilingual Relation Extraction using Compositional Universal Schema
https://github.com/pawan2411/pan-dl_refind
공식 코드https://github.com/pgcool/Cross-sentence-Relation-Extraction-iDepNN
공식 코드Neural Relation Extraction Within and Across Sentence Boundaries
https://github.com/pgcool/Joint-Bootstrapping-Machines
공식 코드Joint Bootstrapping Machines for High Confidence Relation Extraction
https://github.com/phosseini/causal-reasoning
공식 코드Knowledge-Augmented Language Models for Cause-Effect Relation Classification
https://github.com/phucty/MTab
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/phucty/mtab_tool
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/pidinosaur2804/wave-cre-plus-plus
공식 코드https://github.com/pku-tangent/docred-fe
공식 코드DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
https://github.com/pku-yuangroup/languagebind
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.