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        <title>Non-Deterministic Segmentation for Chinese Lattice Parsing</title>
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    <name type="personal">
        <namePart type="given">Hai</namePart>
        <namePart type="family">Hu</namePart>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Dakota</namePart>
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    <name type="personal">
        <namePart type="given">Sandra</namePart>
        <namePart type="family">Kübler</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_043</identifier>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Non-Deterministic Segmentation for Chinese Lattice Parsing
%A Hu, Hai
%A Dakota, Daniel
%A Kübler, Sandra
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F hu-etal-2017-non
%X Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.
%R 10.26615/978-954-452-049-6_043
%U https://doi.org/10.26615/978-954-452-049-6_043
%P 316-324

Markdown (Informal)

[Non-Deterministic Segmentation for Chinese Lattice Parsing](https://doi.org/10.26615/978-954-452-049-6_043) (Hu et al., RANLP 2017)

• Non-Deterministic Segmentation for Chinese Lattice Parsing (Hu et al., RANLP 2017)

ACL

• Hai Hu, Daniel Dakota, and Sandra Kübler. 2017. Non-Deterministic Segmentation for Chinese Lattice Parsing. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 316–324, Varna, Bulgaria. INCOMA Ltd..