Cited by 907Natural Language Processing TechniquesTopic ModelingSyntax, Semantics, Linguistic Variation

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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

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Marcheggiani, Diego, Titov, Ivan·2017·Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

DOI 10.18653/v1/d17-1159

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Abstract

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence.It is typically regarded as an important step in the standard NLP pipeline.As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model.We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs.GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence.We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-theart LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.

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Acknowledgements
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Preface
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Advanced Applications in Graph Neural Networks
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Advances
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Deep Learning on Graphs: An Introduction
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Authors: Marcheggiani, D., & Titov, I.
Affiliations: University of Amsterdam (NL)
Corresponding author: Marcheggiani, Diego
ORCID: Titov, Ivan
Venue: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Publisher: Association for Computational Linguistics
Published: 2017
Pages: 1506-1515
DOI: 10.18653/v1/d17-1159
OpenAlex: W2600702321
Cited by: 907
References: 52
Citations by year: 2026: 20 · 2025: 45 · 2024: 60 · 2023: 75 · 2022: 130 · 2021: 211 · 2020: 176 · 2019: 130 · 2018: 56 · 2017: 4
FWCI: 45.78
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
arXiv (Cornell University)
Funders: National Science Foundation
Amazon Web Services
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (639.022.518)
H2020 European Research Council (678254)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
Keywords: semantic role labeling, graph convolutional networks, syntactic dependency trees, sentence encoding, predicate-argument structure, LSTM, Chinese language processing, English language processing

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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

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