ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners

Nicole Peinelt, Maria Liakata, Shu-Kai Hsieh


Abstract
Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. In contrast to previous studies, we argue that the choice of classifiers is highly contextual and train context-aware machine learning models based on a novel publicly available dataset, outperforming previous baselines. We further present use cases for our database and models in an interactive demo system.
Anthology ID:
I17-3011
Volume:
Proceedings of the IJCNLP 2017, System Demonstrations
Month:
November
Year:
2017
Address:
Tapei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–44
Language:
URL:
https://aclanthology.org/I17-3011
DOI:
Bibkey:
Cite (ACL):
Nicole Peinelt, Maria Liakata, and Shu-Kai Hsieh. 2017. ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 41–44, Tapei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners (Peinelt et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-3011.pdf
Code
 wuningxi/ChineseClassifierDataset
Data
Chinese Classifier