natural-language-processing-4

Joint Entity and Relation Extraction

3260 papers • 126 benchmarks • 313 datasets

Scores reported from systems which jointly extract entities and relations.

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Most implemented papers

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Hannaneh Hajishirzi, Yi Luan, Luheng He, Mari Ostendorf•Tue Aug 28 2018

The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links and supports construction of a scientific knowledge graph, which is used to analyze information in scientific literature.

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ACE 2005

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A general framework for information extraction using dynamic span graphs

Hannaneh Hajishirzi, Yi Luan, Luheng He, Mari Ostendorf, David Wadden, A. Shah•Thu Apr 04 2019

This framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains and is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.

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CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

Jiawei Han, Zeqiu Wu, Heng Ji, Xiang Ren, Wenqi He, Meng Qu, Clare R. Voss, T. Abdelzaher•Wed Oct 26 2016

A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other.

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Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Bo Xu, Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, P. Zhou•Tue Jun 06 2017

A novel tagging scheme is proposed that can convert the joint extraction task to a tagging problem, and different end-to-end models are studied to extract entities and their relations directly, without identifying entities and relations separately.

687 0

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Entity, Relation, and Event Extraction with Contextualized Span Representations

Hannaneh Hajishirzi, Yi Luan, David Wadden, Ulme Wennberg•Sat Sep 07 2019

This work examines the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction (called DyGIE++) and achieves state-of-the-art results across all tasks.

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

Markus Eberts, A. Ulges•Mon Sep 16 2019

SpERT, an attention model for span-based joint entity and relation extraction, is introduced, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation.

443 0

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Two Are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

Wei Lu, Jue Wang•Wed Oct 07 2020

It is argued that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process, and proposed is the novel {\em table-sequence encoder} where two different encoder -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.

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Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

Hinrich Schütze, Pankaj Gupta, B. Andrassy•Wed Nov 30 2016

A Table Filling Multi-Task Recurrent Neural Network model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies and shows that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance.

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Adversarial training for multi-context joint entity and relation extraction

Johannes Deleu, Chris Develder, Giannis Bekoulis, T. Demeester•Tue Jul 31 2018

Adversarial training is demonstrated to be a regularization method that allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).

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GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction

Peng-Hsuan Li, Tsu-Jui Fu, Wei-Yun Ma•Sun Jun 30 2019

GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations, outperforms previous work by 3.2% and 5.8% and achieves a new state-of-the-art for relation extraction.

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