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        <title>L1-L2 Parallel Dependency Treebank as Learner Corpus</title>
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        <namePart type="given">John</namePart>
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        <namePart type="given">Keying</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Herman</namePart>
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            <title>Proceedings of the 15th International Conference on Parsing Technologies</title>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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        <name type="personal">
            <namePart type="given">Kenji</namePart>
            <namePart type="family">Sagae</namePart>
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    <abstract>This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank — i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses — can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus re-use and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework.</abstract>
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        <url>https://aclanthology.org/W17-6306/</url>
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%0 Conference Proceedings
%T L1-L2 Parallel Dependency Treebank as Learner Corpus
%A Lee, John
%A Li, Keying
%A Leung, Herman
%Y Miyao, Yusuke
%Y Sagae, Kenji
%S Proceedings of the 15th International Conference on Parsing Technologies
%D 2017
%8 September
%I Association for Computational Linguistics
%C Pisa, Italy
%F lee-etal-2017-l1
%X This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank — i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses — can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus re-use and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework.
%U https://aclanthology.org/W17-6306/
%P 44-49

Markdown (Informal)

[L1-L2 Parallel Dependency Treebank as Learner Corpus](https://aclanthology.org/W17-6306/) (Lee et al., IWPT 2017)

• L1-L2 Parallel Dependency Treebank as Learner Corpus (Lee et al., IWPT 2017)

ACL

• John Lee, Keying Li, and Herman Leung. 2017. L1-L2 Parallel Dependency Treebank as Learner Corpus. In Proceedings of the 15th International Conference on Parsing Technologies, pages 44–49, Pisa, Italy. Association for Computational Linguistics.