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    <titleInfo>
        <title>The Match-Extend serialization algorithm in Multiprecedence</title>
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    <name type="personal">
        <namePart type="given">Maxime</namePart>
        <namePart type="family">Papillon</namePart>
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            <title>Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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        <name type="personal">
            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Kyle</namePart>
            <namePart type="family">Gorman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ryan</namePart>
            <namePart type="family">Cotterell</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Raimy (1999; 2000a; 2000b) proposed a graphical formalism for modeling reduplication, originallymostly focused on phonological overapplication in a derivational framework. This framework is now known as Precedence-based phonology or Multiprecedence phonology. Raimy’s idea is that the segments at the input to the phonology are not totally ordered by precedence. This paper tackles a challenge that arose with Raimy’s work, the development of a deterministic serialization algorithm as part of the derivation of surface forms. The Match-Extend algorithm introduced here requires fewer assumptions and sticks tighter to the attested typology. The algorithm also contains no parameter or constraint specific to individual graphs or topologies, unlike previous proposals. Match-Extend requires nothing except knowing the last added set of links.</abstract>
    <identifier type="citekey">papillon-2021-match</identifier>
    <identifier type="doi">10.18653/v1/2021.sigmorphon-1.3</identifier>
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        <url>https://aclanthology.org/2021.sigmorphon-1.3</url>
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        <date>2021-08</date>
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            <start>23</start>
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%0 Conference Proceedings
%T The Match-Extend serialization algorithm in Multiprecedence
%A Papillon, Maxime
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F papillon-2021-match
%X Raimy (1999; 2000a; 2000b) proposed a graphical formalism for modeling reduplication, originallymostly focused on phonological overapplication in a derivational framework. This framework is now known as Precedence-based phonology or Multiprecedence phonology. Raimy’s idea is that the segments at the input to the phonology are not totally ordered by precedence. This paper tackles a challenge that arose with Raimy’s work, the development of a deterministic serialization algorithm as part of the derivation of surface forms. The Match-Extend algorithm introduced here requires fewer assumptions and sticks tighter to the attested typology. The algorithm also contains no parameter or constraint specific to individual graphs or topologies, unlike previous proposals. Match-Extend requires nothing except knowing the last added set of links.
%R 10.18653/v1/2021.sigmorphon-1.3
%U https://aclanthology.org/2021.sigmorphon-1.3
%U https://doi.org/10.18653/v1/2021.sigmorphon-1.3
%P 23-31

Markdown (Informal)

[The Match-Extend serialization algorithm in Multiprecedence](https://aclanthology.org/2021.sigmorphon-1.3) (Papillon, SIGMORPHON 2021)

• The Match-Extend serialization algorithm in Multiprecedence (Papillon, SIGMORPHON 2021)

ACL

• Maxime Papillon. 2021. The Match-Extend serialization algorithm in Multiprecedence. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 23–31, Online. Association for Computational Linguistics.