Natural Language Processing
 
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Relation Extraction

665 papers with code • 50 benchmarks • 74 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

Libraries

Use these libraries to find Relation Extraction models and implementations

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Subtasks

Most implemented papers

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BioBERT: a pre-trained biomedical language representation model for biomedical text mining

dmis-lab/biobert • • 25 Jan 2019

Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.

19
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LayoutLM: Pre-training of Text and Layout for Document Image Understanding

microsoft/unilm • • 31 Dec 2019

In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.

15
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Matching the Blanks: Distributional Similarity for Relation Learning

plkmo/BERT-Relation-Extraction • • ACL 2019

General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.

13
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

studio-ousia/luke • • EMNLP 2020

In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.

8
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Simplifying Graph Convolutional Networks

Tiiiger/SGC • • 19 Feb 2019

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

7
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Joint entity recognition and relation extraction as a multi-head selection problem

bekou/multihead_joint_entity_relation_extraction • • 20 Apr 2018

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.

6
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Enriching Pre-trained Language Model with Entity Information for Relation Classification

monologg/R-BERT • • 20 May 2019

In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.

6
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SpanBERT: Improving Pre-training by Representing and Predicting Spans

facebookresearch/SpanBERT • • TACL 2020

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.

6
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Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

malllabiisc/RESIDE • • 19 Apr 2018

Relation extraction is the problem of classifying the relationship between two entities in a given sentence.

5
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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.

5
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