Dependency parsing of learner English
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Journal Title
International Journal of Corpus Linguistics
ISSN
1384-6655
Publisher
John Benjamins Publishing Company
Volume
23
Issue
1
Pages
28-54
Type
Article
This Version
AM
Metadata
Показать полную информациюCitation
Huang, Y., Murakami, A., Alexopoulou, D., & Korhonen, A. (2018). Dependency parsing of learner English. International Journal of Corpus Linguistics, 23 (1), 28-54. https://doi.org/10.1075/ijcl.16080.hua
Аннотации
Current syntactic annotation of large-scale learner corpora mainly resorts to “standard parsers” trained on native language data. Understanding how these parsers perform on learner data is important for downstream research and application related to learner language. This study evaluates the performance of multiple standard probabilistic parsers on learner English. Our contributions are three-fold. Firstly, we demonstrate that the common practice of constructing a gold standard – by manually correcting the pre-annotation of a single parser – can introduce bias to parser evaluation. We propose an alternative annotation method which can control for the annotation bias. Secondly, we quantify the influence of learner errors on parsing errors, and identify the learner errors that impact on parsing most. Finally, we compare the performance of the parsers on learner English and native English. Our results have useful implications on how to select a standard parser for learner English.
Identifiers
External DOI: https://doi.org/10.1075/ijcl.16080.hua
This record's URL: https://www.repository.cam.ac.uk/handle/1810/275806
Rights
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