CoNLL 2014 shared task: Grammatical error correction with a syntactic N-gram language model from a big corpora

S. David Hernandez, Hiram Calvo

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    We describe our approach to grammatical error correction presented in the CoNLL Shared Task 2014. Our work is focused on error detection in sentences with a language model based on syntactic tri-grams and bi-grams extracted from dependency trees generated from 90% of the English Wikipedia. Also, we add a naïve module to error correction that outputs a set of possible answers, those sentences are scored using a syntactic n-gram language model. The sentence with the best score is the final suggestion of the system. The system was ranked 11th, evidently this is a very simple approach, but since the beginning our main goal was to test the syntactic n-gram language model with a big corpus to future comparison.

    Original languageEnglish
    Title of host publicationCoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings of the Shared Task
    PublisherAssociation for Computational Linguistics (ACL)
    Pages53-59
    Number of pages7
    ISBN (Electronic)9781941643198
    StatePublished - 1 Jan 2014
    Event18th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014 - Baltimore, United States
    Duration: 26 Jun 201427 Jun 2014

    Publication series

    NameCoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings of the Shared Task

    Conference

    Conference18th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014
    Country/TerritoryUnited States
    CityBaltimore
    Period26/06/1427/06/14

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