Relation-Extraction

All type of Relation Extraction Data has been added to stack, peek it out.

List of Relation Extraction (Named Entity, CNN, DRNN, Distinct Supervision, etc) work located here 🤔 and totally motivated by This guy.

Papers

• Matching the Blanks: Distributional Similarity for Relation Learning [paper] [code]
  • Method : BERTEM+MTB
• Coreferential Reasoning Learning for Language Representation [paper] [code]
  • Method : CorefRoBERTaLarge
• Downstream Model Design of Pre-trained Language Model for Relation Extraction Task [paper] [code]
  • Method : REDN
• RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information [paper] [code]
  • Method : RESIDE
• Classifying Relations by Ranking with Convolutional Neural Networks [paper] [code]
  • Method : CRNN
• MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks [paper] * Method : CNN
• End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models [paper] [code]
  • Method : NER
• Entity, Relation, and Event Extraction with Contextualized Span Representations [paper] [code]
• Relation Extraction Using Distant Supervision: a Survey [paper]
• Global Relation Embedding for Relation Extraction [paper] [code]
• GREG: A Global Level Relation Extraction with Knowledge Graph Embedding [paper]
  • Method : CNN
• Relation Extraction with Explanation
  • DOI : 10.18653/v1/2020.acl-main.579
• End-to-End Relation Extraction using LSTMs on Sequences and Tree Structure [paper]
  • Method : LSTM/RNN
• Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing [paper] [code]
• Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path [paper] [code]
• Semantic Compositionality through Recursive Matrix-Vector Spaces [paper] [code]
  • Method : RNN
• Distant supervision for relation extraction without labeled data [paper] [review]
• Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations [paper] [code]
• Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks [paper] [code]
• Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention[paper][code]
• SpanBERT: Improving pre-training by representing and predicting spans [paper] [code]

Datasets

Kowledge Graphs

• Data Dumps [download]
• Wiki Links Data [download]

Other Dataset

• TACRED: The TAC Relation Extraction Dataset [paper] [Website] [download]
• FewRel: Few-Shot Relation Classification Dataset [paper] [Website]
  • This dataset is a supervised few-shot relation classification dataset. The corpus is Wikipedia and the knowledge base used to annotate the corpus is Wikidata.

Technologies

• grakn ai [Website]
• Amazon Neptune [Website]
• Neo4j [Website]
• Blazegraph [Website]

Videos and Lectures

• Stanford University: CS124, Dan Jurafsky
  • (Video) Week 5: Relation Extraction and Question
• Washington University: CSE517, Luke Zettlemoyer
  • (Slide) Relation Extraction 1
  • (Slide) Relation Extraction 2
• New York University: CSCI-GA.2590, Ralph Grishman
  • (Slide) Relation Extraction: Rule-based Approaches
• Michigan University: Coursera, Dragomir R. Radev
  • (Video) Lecture 48: Relation Extraction
• Virginia University: CS6501-NLP, Kai-Wei Chang
  • (Slide) Lecture 24: Relation Extraction This section has been copied from This super repo

Contributions: Any type of contributions are accepted