Browse SoTA > Natural Language Processing > Relation Extraction

Relation Extraction

160 papers with code · Natural Language Processing

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

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

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

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 #9 on Relation Extraction on ACE 2005 (using extra training data)

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.

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

Knowledge Representation Learning: A Quantitative Review

28 Dec 2018shaoxiongji/awesome-knowledge-graph

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

Simplifying Graph Convolutional Networks

19 Feb 2019Tiiiger/SGC

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

 Ranked #1 on Text Classification on 20NEWS (using extra training data)

GRAPH REGRESSION IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS SKELETON BASED ACTION RECOGNITION TEXT CLASSIFICATION