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Patricia Garrido Teixeira
UNED - LINHD – UCM
pgarri02@ucm.es
25th January 2017
Analyzing poetry databases to develop a metadata
application profile.
Why each language uses a different way of
modelling?
About POSTDATA
•ERC project
•Principal Investigator: Elena González- Blanco
•”Poetry Standardization and Linked Open Data”
The first phase of the project.
Different tasks:
-Analyze the databases
-Analyze the models and
conceptualizations.
Different level:
-Technological
-Philological.
POSTDATA
semantic model
Philological
analysis
Latin Poetry Databases. Metrical Schemes.
Pedecerto (1)
•term in the database: sistema
•term in the website: schema metrico
•term in the semantic model: lineScheme
Pedecerto (2)
http://www.pedecerto.eu/
(search by verse-type)
corpus: classical latin poetry
Pedecerto (3)
D: Dactylic - uu
S: Spondaic - -
Quantitative Analysis
Anacarm. Analecta hymnica medii aevi
http://webserver.erwin-rauner.de/crophius/Analecta_conspectus.htm
Quantitative Analysis
syllable-counting
Corimu. Corpus Rhythmorum Musicum (1)
http://www.corimu.unisi.it/
•term in the database: tipoverso
•term in the website: verse-type
•term in the semantic model: lineScheme
Corimu. Corpus Rhythmorum Musicum (2)
corpus: latin medieval poetry (rhythmic)
Corimu. Corpus Rhythmorum Musicum (3)
(versification)
8p(paroxyton)+7pp(proparoxyton)
Accentual Analysis
Conductus
number of syllables
Accentuation
Rhyme
Conclusions (1)
1) Different ways of representation of similar phenomena
Classical quantitative metrical representation
Medieval syllable-counting based system
Introduction of Romance elements - rhyme
2) Differences because of the evolution of Latin poetry itself.
“Solo quisiera saber,
para apurar mis desvelos”
7 (+ 1)
(8)
“Solo quisiera saber,
para apurar mis desvelos”
7+8p
Conclusions (2)
4) Need of take into account all these differences.
5) Poetry modeling projects with a deep philological analysis
6) Add a new layer of abstraction to transform metrical information into
a machine readable format.
THANK YOU
pgarri02@ucm.es

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