About

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

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Greatest papers with code

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

IJCNLP 2019 thunlp/OpenNRE

OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE).

INFORMATION RETRIEVAL QUESTION ANSWERING RELATION EXTRACTION

BioMegatron: Larger Biomedical Domain Language Model

EMNLP 2020 NVIDIA/NeMo

There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Books.

LANGUAGE MODELLING NAMED ENTITY RECOGNITION QUESTION ANSWERING RELATION EXTRACTION

Knowledge Representation Learning: A Quantitative Review

28 Dec 2018thunlp/OpenKE

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks.

INFORMATION RETRIEVAL KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING QUESTION ANSWERING RECOMMENDATION SYSTEMS RELATION EXTRACTION REPRESENTATION LEARNING TRIPLE CLASSIFICATION

Biomedical Named Entity Recognition at Scale

12 Nov 2020JohnSnowLabs/spark-nlp

Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc.

ENTITY RESOLUTION INFORMATION RETRIEVAL MEDICAL NAMED ENTITY RECOGNITION QUESTION ANSWERING RELATION EXTRACTION

Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction

13 Apr 2020makcedward/nlpaug

Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications.

LANGUAGE MODELLING 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.

ENTITY LINKING ENTITY TYPING KNOWLEDGE GRAPHS LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SENTIMENT ANALYSIS

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

14 Nov 2018huggingface/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.

 Ranked #1 on Relation Extraction on ACE 2005 (Sentence Encoder metric, using extra training data)

MULTI-TASK LEARNING NAMED ENTITY RECOGNITION RELATION EXTRACTION