Encoding verse texts
Identifieur interne : 000421 ( Istex/Corpus ); précédent : 000420; suivant : 000422Encoding verse texts
Auteurs : David Chisholm ; David RobeySource :
- Computers and the Humanities [ 0010-4817 ] ; 1995-03-01.
Abstract
Abstract: This article identifies problems and proposes solutions for encoding verse texts in SGML. It is organized around a series of distinctions and oppositions which the TEI Work Group on Verse regard as significant. These include examination of the formal properties which distinguish verse from prose, followed by discussions of (1) text-searching vs analysis, (2) markup vs algorithms, (3) markup vs transcription, (4) uniformity vs choice, (5) specificity vs generality, (6) metrical convention vs linguistic realization, (7) structural vs non-structural divisions and (8) fidelity vs interpretation. Using German and English verse forms as illustrations, the advantages and disadvantages of pre-line tagging, in-line tagging and feature structure analysis are discussed. We suggest that metrical and rhyme conventions always be tagged at the highest possible level of text divisions.
Url:
DOI: 10.1007/BF01830704
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ISTEX:94FFE7A1BE72B30E5C414FE3CEFC740AAAF40E29Le document en format XML
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ContainsESM="No"><ArticleID>BF01830704</ArticleID><ArticleDOI>10.1007/BF01830704</ArticleDOI><ArticleSequenceNumber>1</ArticleSequenceNumber><ArticleTitle Language="En">Encoding verse texts</ArticleTitle><ArticleCategory>Part III: Encoding Specific Text Types</ArticleCategory><ArticleFirstPage>99</ArticleFirstPage><ArticleLastPage>111</ArticleLastPage><ArticleHistory><RegistrationDate><Year>2005</Year><Month>5</Month><Day>20</Day></RegistrationDate></ArticleHistory><ArticleCopyright><CopyrightHolderName>Kluwer Academic Publishers</CopyrightHolderName><CopyrightYear>1995</CopyrightYear></ArticleCopyright><ArticleGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ArticleGrants><ArticleContext><JournalID>10579</JournalID><VolumeIDStart>29</VolumeIDStart><VolumeIDEnd>29</VolumeIDEnd><IssueIDStart>2</IssueIDStart><IssueIDEnd>2</IssueIDEnd></ArticleContext></ArticleInfo><ArticleHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Chisholm</FamilyName></AuthorName><Contact><Email>chisholm@ccit.arizona.edu</Email></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Robey</FamilyName></AuthorName><Contact><Email>robey@vax.ox.ac.uk</Email></Contact></Author><Affiliation ID="Aff1"><OrgDivision>Department of German Studies</OrgDivision><OrgName>University of Arizona</OrgName><OrgAddress><Postcode>85721</Postcode><City>Tucson</City><State>AZ</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Department of Italian Studies</OrgDivision><OrgName>The University of Manchester</OrgName><OrgAddress><Street>Oxford Road</Street><City>Manchester</City><Country>UK</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This article identifies problems and proposes solutions for encoding verse texts in SGML. It is organized around a series of distinctions and oppositions which the TEI Work Group on Verse regard as significant. These include examination of the formal properties which distinguish verse from prose, followed by discussions of (1) text-searching vs analysis, (2) markup vs algorithms, (3) markup vs transcription, (4) uniformity vs choice, (5) specificity vs generality, (6) metrical convention vs linguistic realization, (7) structural vs non-structural divisions and (8) fidelity vs interpretation. Using German and English verse forms as illustrations, the advantages and disadvantages of pre-line tagging, in-line tagging and feature structure analysis are discussed. We suggest that metrical and rhyme conventions always be tagged at the highest possible level of text divisions.</Para></Abstract><KeywordGroup Language="En"><Heading>Key words</Heading><Keyword>SGML</Keyword><Keyword>verse</Keyword><Keyword>poetry</Keyword><Keyword>tagging</Keyword><Keyword>text encoding</Keyword><Keyword>TEI</Keyword></KeywordGroup><ArticleNote Type="Misc"><SimplePara>David Chisholm is Professor of German Studies at the University of Arizona. His interests include German lyric poetry and verse drama, metrics and versification, linguistic and computational approaches to literature, and phonological and prosodic aspects of literary style. His publications include “Computer-assisted Research in German Language and Literature” in the<Emphasis Type="Italic">German Quarterly</Emphasis> (1985),<Emphasis Type="Italic">Verskonkordanz zu Goethes “Faust, Erster Teil”</Emphasis> (with Stephen P. Sondrup, 1986), “Phonological Patterning in Twentieth Century German Verse and Prose” in<Emphasis Type="Italic">Descriptio Linguistica</Emphasis> (1986) and “Lexicality and the Versification of Johann Heinrich Voss” in<Emphasis Type="Italic">Insights in Germanic Linguistics</Emphasis> (1994).</SimplePara></ArticleNote><ArticleNote Type="Misc"><SimplePara>David Robey is Professor of Italian at Manchester University and Dean of the Faculty of Arts. He has worked on Italian Renaissance Humanism, modern literary theory (editor and author with Ann Jefferson of<Emphasis Type="Italic">Modern Literary Theory: A Comparative Introduction</Emphasis>), and on the computer analysis of medieval and Renaissance narrative poetry. He is currently engaged in a metrical study of the<Emphasis Type="Italic">Divine Comedy</Emphasis>, and was Chair of the Verse Work Group of the Text Encoding Initiative.</SimplePara></ArticleNote></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Encoding verse texts</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Encoding verse texts</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Chisholm</namePart><affiliation>Department of German Studies, University of Arizona, 85721, Tucson, AZ, USA</affiliation><affiliation>E-mail: chisholm@ccit.arizona.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">David</namePart><namePart type="family">Robey</namePart><affiliation>Department of Italian Studies, The University of Manchester, Oxford Road, Manchester, UK</affiliation><affiliation>E-mail: robey@vax.ox.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper"></genre><originInfo><publisher>Kluwer Academic Publishers</publisher><place><placeTerm type="text">Dordrecht</placeTerm></place><dateIssued encoding="w3cdtf">1995-03-01</dateIssued><copyrightDate encoding="w3cdtf">1995</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: This article identifies problems and proposes solutions for encoding verse texts in SGML. It is organized around a series of distinctions and oppositions which the TEI Work Group on Verse regard as significant. These include examination of the formal properties which distinguish verse from prose, followed by discussions of (1) text-searching vs analysis, (2) markup vs algorithms, (3) markup vs transcription, (4) uniformity vs choice, (5) specificity vs generality, (6) metrical convention vs linguistic realization, (7) structural vs non-structural divisions and (8) fidelity vs interpretation. Using German and English verse forms as illustrations, the advantages and disadvantages of pre-line tagging, in-line tagging and feature structure analysis are discussed. 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