Dependency-Based Semantic Role Labeling using Convolutional Neural Networks

Foland, William; Martin, James H.

doi:10.18653/v1/s15-1033

Cited by 31 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other recent work which considered incorporation of syntactic information in neural SRL models include: FitzGerald et al (2015) who use standard syntactic features within an MLP calculating potentials of a CRF model; Roth and Lapata (2016) who enriched standard features for SRL with LSTM representations of syntactic paths between arguments and predicates; Lei et al (2015) who relied on low-rank tensor factorizations for modeling syntax. Also Foland and Martin (2015) used (nongraph) convolutional networks and provided syntactic features as input. A very different line of research, but with similar goals to ours (i.e.…”

Section: Related Workmentioning

confidence: 99%

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Marcheggiani¹,

Titov²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

723

402

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Marcheggiani¹,

Titov²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

723

402

View full text Add to dashboard Cite

show abstract

“…Along with the the impressive success of deep neural networks Cai and Zhao, 2016;Wang et al, 2016b,a;, a series of neural SRL systems have been proposed. For instance, Foland and Martin (2015) presented a semantic role labeler using convolutional and time-domain neural networks. FitzGerald et al (2015) exploited neural network to jointly embed arguments and semantic roles, akin to the work (Lei et al, 2015), which induced a compact feature representation applying tensor-based approach.…”

Section: Related Workmentioning

confidence: 99%

A Unified Syntax-aware Framework for Semantic Role Labeling

Li¹,

He²,

Cai³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.

show abstract

“…One popular design is a multistage pipeline that identifies argument spans and then labels them. Another alternative is BIO-style classification of argument words, either with conventional classifiers or with neural networks (e.g., Collobert et al, 2011;Foland and Martin, 2015). More recent systems (e.g., Roth and Lapata, 2016) use neural networks to score and label possible argument spans or heads.…”

Section: Introductionmentioning

confidence: 99%

DeepCx: A transition-based approach for shallow semantic parsing with complex constructional triggers

Dunietz¹,

Carbonell²,

Levin³

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

This paper introduces the SURFACE CONSTRUCTION LABELING (SCL) task, which expands the coverage of Shallow Semantic Parsing (SSP) to include frames triggered by complex constructions. We present DeepCx, a neural, transition-based system for SCL. As a test case for the approach, we apply DeepCx to the task of tagging causal language in English, which relies on a wider variety of constructions than are typically addressed in SSP. We report substantial improvements over previous tagging efforts on a causal language dataset. We also propose ways DeepCx could be extended to still more difficult constructions and to other semantic domains once appropriate datasets become available.

show abstract

Dependency-Based Semantic Role Labeling using Convolutional Neural Networks

Cited by 31 publications

References 15 publications

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

A Unified Syntax-aware Framework for Semantic Role Labeling

DeepCx: A transition-based approach for shallow semantic parsing with complex constructional triggers

Contact Info

Product

Resources

About