Relation Extraction
309 papers with code • 27 benchmarks • 37 datasets
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 Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
SemEval-2010 Task 8
TACRED
WebNLG
FewRel
ACE 2005
DocRED
SciERC
DDI
Subtasks
Greatest papers with code
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
stanfordnlp/stanza
•
•
ACL 2020
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
thunlp/OpenNRE
•
•
Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data.
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
thunlp/OpenNRE
•
•
IJCNLP 2019
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE).
BioMegatron: Larger Biomedical Domain Language Model
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.
Ranked #1 on
Named Entity Recognition
on BC5CDR-disease
Knowledge Representation Learning: A Quantitative Review
thunlp/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.
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction
Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications.
The Natural Language Decathlon: Multitask Learning as Question Answering
salesforce/decaNLP
•
•
ICLR 2019
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
dmis-lab/biobert
•
•
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #1 on
Named Entity Recognition
on NCBI-disease
ERNIE: Enhanced Language Representation with Informative Entities
thunlp/ERNIE
•
•
ACL 2019
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.
Ranked #1 on
Entity Linking
on FIGER
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.
Ranked #7 on
Relation Extraction
on ACE 2005
(using extra training data)
