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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Open accessAbstract
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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Metadata
- 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
- Published
- 2017
- Pages
- 1506-1515
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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