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/eternityyw/llm_healthcare
공식 코드https://github.com/evolvinglmms-lab/lmms-eval
공식 코드https://github.com/facebookresearch/SpanBERT
공식 코드SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/facebookresearch/imagebind
공식 코드https://github.com/fc-liu/mcmn
공식 코드Pre-training to Match for Unified Low-shot Relation Extraction
https://github.com/fduyjx/rsman
공식 코드Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction
https://github.com/fenchri/walk-based-re
공식 코드https://github.com/fmtumbuka/relationembeddings
공식 코드Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
https://github.com/foxlf823/sodner
공식 코드A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
https://github.com/fractalego/pynsett
공식 코드https://github.com/frankdarkluo/sols
공식 코드Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
https://github.com/frankkramer-lab/GERNERMED
공식 코드https://github.com/freesunshine0316/dep-forest-re
공식 코드Leveraging Dependency Forest for Neural Medical Relation Extraction
https://github.com/freesunshine0316/nary-grn
공식 코드https://github.com/fyshelab/qa-zre
공식 코드Weakly-Supervised Questions for Zero-Shot Relation Extraction
https://github.com/fyubang/direct-ie
공식 코드Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning
https://github.com/geetickachauhan/relation-extraction
공식 코드REflex: Flexible Framework for Relation Extraction in Multiple Domains
https://github.com/ghazalkhalighinejad/pncextract
공식 코드Extracting Polymer Nanocomposite Samples from Full-Length Documents
https://github.com/gjiaying/zs-ska
공식 코드Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
https://github.com/gjiaying/zslrc
공식 코드https://github.com/gjorgjinac/food_chem
공식 코드https://github.com/gmpoli/electramed
공식 코드ELECTRAMed: a new pre-trained language representation model for biomedical NLP
https://github.com/go0day/tprec
공식 코드Time-aware Path Reasoning on Knowledge Graph for Recommendation
https://github.com/google/example_extrapolation
공식 코드https://github.com/gstoica27/JRRELP
공식 코드Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
https://github.com/gstoica27/Re-TACRED
공식 코드https://github.com/guanw-pku/oed
공식 코드OED: Towards One-stage End-to-End Dynamic Scene Graph Generation
https://github.com/guardiaum/BERT-SIDE
공식 코드Distantly-Supervised Neural Relation Extraction with Side Information using BERT
https://github.com/gucorpling/discodisco
공식 코드https://github.com/gucorpling/gum2pdtb
공식 코드https://github.com/guojiapub/pemscl
공식 코드Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction
https://github.com/guoqianyu-alberta/rankdnn
공식 코드https://github.com/gxxxzhang/siamese-ure
공식 코드Siamese Representation Learning for Unsupervised Relation Extraction
https://github.com/gyeongmin47/kochet
공식 코드KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks
https://github.com/hadyelsahar/relation-discovery-2-entities
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.