The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Bootstrapping parsers via syntactic projection across parallel texts
Abstract
Broad coverage, high quality parsers are available for only a handful of languages. A prerequisite for developing broad coverage parsers for more languages is the annotation of text with the desired linguistic representations (also known as “treebanking”). However, syntactic annotation is a labor intensive and time-consuming process, and it is difficult to find linguistically annotated text in sufficient quantities. In this article, we explore using parallel text to help solving the problem of creating syntactic annotation in more languages. The central idea is to annotate the English side of a parallel corpus, project the analysis to the second language, and then train a stochastic analyzer on the resulting noisy annotations. We discuss our background assumptions, describe an initial study on the “projectability” of syntactic relations, and then present two experiments in which stochastic parsers are developed with minimal human intervention via projection from English.
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Cites
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Cited by
Improving Vector Space Word Representations Using Multilingual Correlation
Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1049 · Cited by 620
Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser
Duong, L., Cohn, T., Bird, S., & Cook, P. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.3115/v1/p15-2139 · Cited by 402
Challenges and Strategies in Cross-Cultural NLP
Hershcovich, D., Frank, S., Lent, H., de Lhoneux, M., Abdou, M., Brandl, S., Bugliarello, E., Cabello Piqueras, L., Chalkidis, I., Cui, R., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.482 · Cited by 99
Controlling hallucinations at word level in data-to-text generation
Rebuffel, C., Roberti, M., Soulier, L., Scoutheeten, G., Cancelliere, R., & Gallinari, P. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00801-4 · Cited by 50
Metadata
- Authors
- HWA, R., RESNIK, P., WEINBERG, A., CABEZAS, C., & KOLAK, O.
- Venue
- Natural Language Engineering
- Publisher
- Cambridge University Press (CUP)
- Published
- 2005-09
- Volume
- 11
- Issue
- 3
- Pages
- 311-325
- ISSN
- 1351-3249 (ISSN-L)
- 1469-8110
- OpenAlex
- W2143954309
- Cited by
- 340
- References
- 31
- FWCI
- 11.68
- Type
- journal article
- Language
- en
- Open access
- closed
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