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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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Liu, C., Gao, Y., & Fang, Z. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16670-6 · Cited by 4
Graph-Based Attention Networks for Aspect Level Sentiment Analysis
Chen, J., Hou, H., Ji, Y., Gao, J., & Bai, T. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00164 · Cited by 5
Improving Relation Classification by Incorporating Dependency and Semantic Information
Deng, K., & Wu, S. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207292 · Cited by 4
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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