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Multilingual dependency analysis with a two-stage discriminative parser
Open accessAbstract
We present a two-stage multilingual dependency parser and evaluate it on 13 diverse languages. The first stage is based on the unlabeled dependency parsing models described by McDonald and Pereira (2006) augmented with morphological features for a subset of the languages. The second stage takes the output from the first and labels all the edges in the dependency graph with appropriate syntactic categories using a globally trained sequence classifier over components of the graph. We report results on the CoNLL-X shared task (Buchholz et al., 2006) data sets and present an error analysis.
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The average length of an Arabic sentence is around 35 words.
“The average length of Arabic sentence is widely higher than other languages, it is around 35 words per sentence [38].”
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Metadata
- Authors
- McDonald, R., Lerman, K., & Pereira, F.
- Affiliations
- University of Pennsylvania (US)
- ORCID
- Pereira, Fernando
- Venue
- Proceedings of the Tenth Conference on Computational Natural Language Learning - CoNLL-X '06
- Published
- 2006
- Pages
- 216
- OpenAlex
- W2143541936
- Cited by
- 223
- References
- 22
- Citations by year
- 2022: 3 · 2021: 5 · 2020: 8 · 2019: 2 · 2018: 12 · 2017: 8 · 2016: 9 · 2015: 15 · 2014: 14 · 2013: 25 · 2012: 29
- FWCI
- 13.17
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Conference on Computational Natural Language Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- multilingual dependency parsing, morphological features, sequence classification, error analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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