Abstract
Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency. We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence on the nature of the challenges posed by indefinite pronouns to English learners. We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.
Original language | English |
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Title of host publication | CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference |
Publisher | Association for Computational Linguistics |
Pages | 77-86 |
Number of pages | 10 |
ISBN (Electronic) | 9781950737727 |
State | Published - 2019 |
Externally published | Yes |
Event | 23rd Conference on Computational Natural Language Learning, CoNLL 2019 - Hong Kong, China Duration: 3 Nov 2019 → 4 Nov 2019 |
Publication series
Name | CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference |
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Conference
Conference | 23rd Conference on Computational Natural Language Learning, CoNLL 2019 |
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Country/Territory | China |
City | Hong Kong |
Period | 3/11/19 → 4/11/19 |
Bibliographical note
Publisher Copyright:© 2019 Association for Computational Linguistics.
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Computational Theory and Mathematics