Natural Language Processing
 
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Relation Extraction

696 papers with code • 49 benchmarks • 75 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

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Use these libraries to find Relation Extraction models and implementations

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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.

18
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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.

9
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Are Transformers Effective for Time Series Forecasting?

WenjieDu/PyPOTS • • 26 May 2022

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.

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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LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

microsoft/unilm • • ACL 2021

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.

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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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.

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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Code
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