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    <titleInfo>
        <title>Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing</title>
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    <name type="personal">
        <namePart type="given">Weiwei</namePart>
        <namePart type="family">Sun</namePart>
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    <abstract>We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00229</identifier>
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        <date>2013</date>
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%0 Journal Article
%T Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing
%A Sun, Weiwei
%A Wan, Xiaojun
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F sun-wan-2013-data
%X We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.
%R 10.1162/tacl_a_00229
%U https://aclanthology.org/Q13-1025
%U https://doi.org/10.1162/tacl_a_00229
%P 301-314

Markdown (Informal)

[Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing](https://aclanthology.org/Q13-1025) (Sun & Wan, TACL 2013)

• Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing (Sun & Wan, TACL 2013)

ACL

• Weiwei Sun and Xiaojun Wan. 2013. Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing. Transactions of the Association for Computational Linguistics, 1:301–314.