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    <titleInfo>
        <title>Language Independent Dependency to Constituent Tree Conversion</title>
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    <name type="personal">
        <namePart type="given">Young-Suk</namePart>
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        <namePart type="given">Zhiguo</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique works in two steps. First, a partial constituent tree is derived from a dependency tree with a very simple deterministic algorithm that is both language and dependency type independent. Second, a complete high accuracy constituent tree is derived with a constraint-based parser, which uses the partial constituent tree as external constraints. Evaluated on Section 22 of the WSJ Treebank, the technique achieves the state-of-the-art conversion F-score 95.6. When applied to English Universal Dependency treebank and German CoNLL2006 treebank, the converted treebanks added to the human-annotated constituent parser training corpus improve parsing F-scores significantly for both languages.</abstract>
    <identifier type="citekey">lee-wang-2016-language</identifier>
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        <url>https://aclanthology.org/C16-1041</url>
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%0 Conference Proceedings
%T Language Independent Dependency to Constituent Tree Conversion
%A Lee, Young-Suk
%A Wang, Zhiguo
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F lee-wang-2016-language
%X We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique works in two steps. First, a partial constituent tree is derived from a dependency tree with a very simple deterministic algorithm that is both language and dependency type independent. Second, a complete high accuracy constituent tree is derived with a constraint-based parser, which uses the partial constituent tree as external constraints. Evaluated on Section 22 of the WSJ Treebank, the technique achieves the state-of-the-art conversion F-score 95.6. When applied to English Universal Dependency treebank and German CoNLL2006 treebank, the converted treebanks added to the human-annotated constituent parser training corpus improve parsing F-scores significantly for both languages.
%U https://aclanthology.org/C16-1041
%P 421-428

Markdown (Informal)

[Language Independent Dependency to Constituent Tree Conversion](https://aclanthology.org/C16-1041) (Lee & Wang, COLING 2016)

• Language Independent Dependency to Constituent Tree Conversion (Lee & Wang, COLING 2016)

ACL

• Young-Suk Lee and Zhiguo Wang. 2016. Language Independent Dependency to Constituent Tree Conversion. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 421–428, Osaka, Japan. The COLING 2016 Organizing Committee.