Natural Language Processing
 
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Relation Extraction

668 papers with code • 49 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

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Entity, Relation, and Event Extraction with Contextualized Span Representations

dwadden/dygiepp • • IJCNLP 2019

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.

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Span-based Joint Entity and Relation Extraction with Transformer Pre-training

markus-eberts/spert • • 17 Sep 2019

The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.

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Generalizing Natural Language Analysis through Span-relation Representations

jzbjyb/SpanRel • • ACL 2020

Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.

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Dialogue-Based Relation Extraction

nlpdata/dialogre • • ACL 2020

We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.

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Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!

btaille/sincere • • EMNLP 2020

Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.

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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

ucinlp/autoprompt • • EMNLP 2020

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining.

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Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

PaddlePaddle/Research • • 20 Feb 2021

Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.

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KLUE: Korean Language Understanding Evaluation

KLUE-benchmark/KLUE • 20 May 2021

We introduce Korean Language Understanding Evaluation (KLUE) benchmark.

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Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction

tonytan48/re-docred • 25 May 2022

We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.

3
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TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations

naushadzaman/tempeval3_toolkit • 22 Jun 2012

We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise.

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