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        <title>Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings</title>
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        <namePart type="given">Masahiro</namePart>
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        <namePart type="given">Yuya</namePart>
        <namePart type="family">Sakaizawa</namePart>
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    <name type="personal">
        <namePart type="given">Mamoru</namePart>
        <namePart type="family">Komachi</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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    <abstract>In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.</abstract>
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        <url>https://aclanthology.org/I17-1005</url>
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%0 Conference Proceedings
%T Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings
%A Kaneko, Masahiro
%A Sakaizawa, Yuya
%A Komachi, Mamoru
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F kaneko-etal-2017-grammatical
%X In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.
%U https://aclanthology.org/I17-1005
%P 40-48

Markdown (Informal)

[Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings](https://aclanthology.org/I17-1005) (Kaneko et al., IJCNLP 2017)

• Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings (Kaneko et al., IJCNLP 2017)

ACL

• Masahiro Kaneko, Yuya Sakaizawa, and Mamoru Komachi. 2017. Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 40–48, Taipei, Taiwan. Asian Federation of Natural Language Processing.