Relation Extraction
State-of-the-art leaderboards
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Subtasks
Greatest papers with code
The Natural Language Decathlon: Multitask Learning as Question Answering
ICLR 2019
• salesforce/decaNLP
• 
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
• huggingface/hmtl
•
The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.
MULTI-TASK LEARNING NAMED ENTITY RECOGNITION RELATION EXTRACTION
ERNIE: Enhanced Language Representation with Informative Entities
ACL 2019
• thunlp/ERNIE
•
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
SOTA for Relation Extraction on FewRel
ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE SENTIMENT ANALYSIS
Simplifying Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
SOTA for Text Classification on Ohsumed
IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS TEXT CLASSIFICATION
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• dmis-lab/biobert
• 
However, as deep learning models require a large amount of training data, applying deep learning to biomedical text mining is often unsuccessful due to the lack of training data in biomedical fields.
MEDICAL NAMED ENTITY RECOGNITION MEDICAL RELATION EXTRACTION QUESTION ANSWERING RELATION EXTRACTION SENTENCE CLASSIFICATION
Indirect Supervision for Relation Extraction using Question-Answer Pairs
However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
SciBERT: Pretrained Contextualized Embeddings for Scientific Text
• allenai/scibert
•
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
SOTA for Named Entity Recognition on NCBI-disease (using extra training data)
CITATION INTENT CLASSIFICATION DEPENDENCY PARSING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION
Context-Aware Representations for Knowledge Base Relation Extraction
EMNLP 2017
• UKPLab/emnlp2017-relation-extraction
•
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation.