Abstract
According to the analysis of Cambridge Learner Corpus, using a wrong verb is the most common type of grammatical errors. This paper describes Verb Replacer, a system for detecting and correcting potential verb errors in a given sentence. In our approach, alternative verbs are considered to replace the verb based on an error-annotated corpus and verb-object collocations. The method involves applying regression on channel models, parsing the sentence, identifying the verbs, retrieving a small set of alternative verbs, and evaluating each alternative. Our method combines and improves channel and language models, resulting in high recall of detecting and correcting verb misuse.- Anthology ID:
- I17-3013
- Volume:
- Proceedings of the IJCNLP 2017, System Demonstrations
- Month:
- November
- Year:
- 2017
- Address:
- Tapei, Taiwan
- Editors:
- Seong-Bae Park, Thepchai Supnithi
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 49–52
- Language:
- URL:
- https://aclanthology.org/I17-3013
- DOI:
- Cite (ACL):
- Yu-Hsuan Wu, Jhih-Jie Chen, and Jason Chang. 2017. Verb Replacer: An English Verb Error Correction System. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 49–52, Tapei, Taiwan. Association for Computational Linguistics.
- Cite (Informal):
- Verb Replacer: An English Verb Error Correction System (Wu et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/I17-3013.pdf