Relation Extraction Edit Task
Natural Language Processing
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
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Subtasks
Datasets
Greatest papers with code
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
ACL 2020
• stanfordnlp/stanza
• 
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
COREFERENCE RESOLUTION DEPENDENCY PARSING LEMMATIZATION NAMED ENTITY RECOGNITION RELATION EXTRACTION TOKENIZATION
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
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
• 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.
INFORMATION RETRIEVAL KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING QUESTION ANSWERING RECOMMENDATION SYSTEMS RELATION EXTRACTION REPRESENTATION LEARNING TRIPLE CLASSIFICATION
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.
Ranked #2 on
Natural Language Inference
on MultiNLI
(Accuracy metric)
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
Biomedical Named Entity Recognition at Scale
Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc.
Ranked #1 on
Named Entity Recognition
on JNLPBA
ENTITY RESOLUTION INFORMATION RETRIEVAL MEDICAL NAMED ENTITY RECOGNITION QUESTION ANSWERING RELATION EXTRACTION
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.
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
LANGUAGE MODELLING MEDICAL NAMED ENTITY RECOGNITION MEDICAL RELATION EXTRACTION QUESTION ANSWERING RELATION EXTRACTION SENTENCE CLASSIFICATION
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.
Ranked #1 on
Entity Linking
on FIGER
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
• 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 #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric, using extra
training data)
MULTI-TASK LEARNING NAMED ENTITY RECOGNITION RELATION EXTRACTION