The DOI matches its OpenAlex record; 2 of the 2 references it cites were found in a registry or an index.
Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks
Abstract
Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also defines two dependency graph representations which contain additional and augmented relations that explicitly capture otherwise implicit relations between content words. In this paper, we revisit and extend these dependency graph representations in light of the recent Universal Dependencies (UD) initiative and provide a detailed account of an enhanced and an enhanced++ English UD representation. We further present a converter from constituency to basic, i.e., strict surface structure, UD trees, and a converter from basic UD trees to enhanced and enhanced++ English UD graphs. We release both converters as part of Stanford CoreNLP and the Stanford Parser.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Leveraging Linguistic Structure For Open Domain Information Extraction
Angeli, G., Johnson Premkumar, M. J., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1034 · Cited by 725
Self-citation
Cited by
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Nivre, J., de Marneffe, M.-C., Ginter, F., Hajič, J., Manning, C. D., Pyysalo, S., Schuster, S., Tyers, F. M., & Zeman, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10643 · Cited by 325
Self-citationWu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.102 · Cited by 58
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages
Ravfogel, S., Goldberg, Y., & Linzen, T. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1356 · Cited by 39
Agmon, G., Pradhan, S., Ash, S., Nevler, N., Liberman, M., Grossman, M., & Cho, S. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-23-00009 · Cited by 17
Metadata
- Authors
- Schuster, S., & Manning, C. D.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2016-05-01
- Pages
- 2371-2378
- OpenAlex
- W2571842322
- Cited by
- 224
- References
- 2
- FWCI
- 21.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Schuster, S., & Manning, C. D. (2016). Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks. Language Resources and Evaluation Conference (LREC). https://doi.org/10.63317/3fjxjut9audq
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