@inproceedings{wu-etal-2017-verb,
    title = "Verb Replacer: An {E}nglish Verb Error Correction System",
    author = "Wu, Yu-Hsuan  and
      Chen, Jhih-Jie  and
      Chang, Jason",
    editor = "Park, Seong-Bae  and
      Supnithi, Thepchai",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/I17-3013/",
    pages = "49--52",
    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."
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    <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.</abstract>
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%0 Conference Proceedings
%T Verb Replacer: An English Verb Error Correction System
%A Wu, Yu-Hsuan
%A Chen, Jhih-Jie
%A Chang, Jason
%Y Park, Seong-Bae
%Y Supnithi, Thepchai
%S Proceedings of the IJCNLP 2017, System Demonstrations
%D 2017
%8 November
%I Association for Computational Linguistics
%C Tapei, Taiwan
%F wu-etal-2017-verb
%X 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.
%U https://aclanthology.org/I17-3013/
%P 49-52
Markdown (Informal)
[Verb Replacer: An English Verb Error Correction System](https://aclanthology.org/I17-3013/) (Wu et al., IJCNLP 2017)
ACL