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Universal Stanford dependencies: A cross-linguistic typology
Open accessAbstract
Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones.We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added.We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology.We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.
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Cites
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Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220230 · Cited by 793
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SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986
Large-scale evidence of dependency length minimization in 37 languages
Futrell, R., Mahowald, K., & Gibson, E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1502134112 · Cited by 455
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · arXiv · doi:10.48550/arxiv.1901.07031 · Cited by 392
Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval
Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., & Manning, C. D. (2015) · Proceedings of the Fourth Workshop on Vision and Language · doi:10.18653/v1/w15-2812 · Cited by 332
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Metadata
- Authors
- de Marneffe, M.-C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., & Manning, C. D.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2014-05-01
- Pages
- 4585-4592
- OpenAlex
- W1962622193
- Cited by
- 456
- References
- 1
- FWCI
- 32.22
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- de Marneffe, M.-C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., & Manning, C. D. (2014). Universal Stanford dependencies: A cross-linguistic typology. Language Resources and Evaluation Conference (LREC). https://doi.org/10.63317/4ok3zdjx8t96
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