The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
Open accessAbstract
We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage—the pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F 1 score among 19 participants.
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Head-Driven Statistical Models for Natural Language Parsing
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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Marcheggiani, D., & Titov, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1159 · Cited by 907
The Natural Language Decathlon: Multitask Learning as Question Answering
McCann, B., Keskar, N. S., Xiong, C., & Socher, R. (2018) · arXiv · doi:10.48550/arxiv.1806.08730 · Cited by 448
Automatic generation of short answer questions for reading comprehension assessment
HUANG, Y., & HE, L. (2016) · Natural Language Engineering · doi:10.1017/s1351324915000455 · Cited by 30
Solving General Arithmetic Word Problems
Roy, S., & Roth, D. (2016) · arXiv · doi:10.48550/arxiv.1608.01413 · Cited by 275
“This has been successfully used to incorporate world knowledge at inference time, as well as getting around the need for large amounts of jointly annotated data for structured prediction (Roth and Yih, 2005; Punyakanok et al., 2005; Punyakanok et al., 2008; Clarke and Lapata, 2006; Barzilay and Lapata, 2006; Roy et al…”
MethodSelf-citation
Metadata
- Authors
- Punyakanok, V., Roth, D., & Yih, W.-T.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2008-06
- Volume
- 34
- Issue
- 2
- Pages
- 257-287
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2038324640
- Cited by
- 391
- References
- 36
- FWCI
- 34.45
- Type
- journal article
- Language
- en
- Open access
- bronze
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