https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
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/sanyabt/np-kg
공식 코드Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions
https://github.com/sciencenlp-lab/dimb-re
공식 코드DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations
https://github.com/sebschu/naacl-gapping
공식 코드Sentences with Gapping: Parsing and Reconstructing Elided Predicates
https://github.com/sefeoglu/coronanews-ner
공식 코드https://github.com/sefeoglu/rag4re
공식 코드https://github.com/seukgcode/fastre
공식 코드https://github.com/sgarg87/HFR
공식 코드https://github.com/shacharosn/CRE
공식 코드Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
https://github.com/shanzhenren/CoType
공식 코드CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
https://github.com/shashank140195/raredis
공식 코드https://github.com/shawx825/hidialog
공식 코드Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
https://github.com/shengwei-tian/dependency-parser-visualization
공식 코드AutoLLM-CARD: Towards a Description and Landscape of Large Language Models
https://github.com/shirley-wu/text_to_table
공식 코드https://github.com/simpleparadox/re_656
공식 코드Relational Extraction on Wikipedia Tables using Convolutional and Memory Networks
https://github.com/skyuforever/cre-llm
공식 코드https://github.com/snowood1/zero-shot-plover
공식 코드Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification
https://github.com/solkx/m3d
공식 코드https://github.com/soummyaah/finred
공식 코드FinRED: A Dataset for Relation Extraction in Financial Domain
https://github.com/spilioeve/Definition-Frames
공식 코드Definition Frames: Using Definitions for Hybrid Concept Representations
https://github.com/stanfordnlp/stanza
공식 코드Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/studio-ousia/luke
공식 코드LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/suamin/meddistant19
공식 코드MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
https://github.com/suamin/umls-medline-distant-re
공식 코드A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
https://github.com/sunlab-osu/REDS2
공식 코드Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction
https://github.com/sunlab-osu/TURL
공식 코드https://github.com/synapse-developpement/Discovery
공식 코드Mining Discourse Markers for Unsupervised Sentence Representation Learning
https://github.com/syuoni/eznlp
공식 코드A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text
https://github.com/syxu828/QuestionAnsweringOverFB
공식 코드Question Answering on Freebase via Relation Extraction and Textual Evidence
https://github.com/takase/relPatSim
공식 코드Composing Distributed Representations of Relational Patterns
https://github.com/tanfiona/unicausal
공식 코드UniCausal: Unified Benchmark and Repository for Causal Text Mining
https://github.com/tangxuemei1995/chisiec
공식 코드CHisIEC: An Information Extraction Corpus for Ancient Chinese History
https://github.com/teddy-li/chineseentgraph
공식 코드https://github.com/thanhnx12/cre-via-mmi
공식 코드Preserving Generalization of Language models in Few-shot Continual Relation Extraction
https://github.com/thu-bpm/gradlre
공식 코드Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
https://github.com/thu-bpm/hiure
공식 코드HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.