APCD
A dataset of Arabic poetry containing 1,831,770 along with there meters. 
Helwan University
Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis
meter classification
nan
Waleed A. Yousefa,Omar M. Ibrahime,Taha M. Madboulya, Moustafa A. Mahmoud
Recognizing a piece of writing as a poem or prose is
usually easy for the majority of people; however, only specialists
can determine which meter a poem belongs to. In this paper, we
build Recurrent Neural Network (RNN) models that can classify
poems according to their meters from plain text. The input text
is encoded at the character level and directly fed to the models
without feature handcrafting. This is a step forward for machine
understanding and synthesis of languages in general, and Arabic
language in particular. Among the 16 poem meters of Arabic and the 4 meters
of English the networks were able to correctly classify poem
with an overall accuracy of 96.38% and 82.31% respectively.
The poem datasets used to conduct this research were massive,
over 1.5 million of verses, and were crawled from different
nontechnical sources, almost Arabic and English literature sites,
and in different heterogeneous and unstructured formats. These
datasets are now made publicly available in clean, structured,
and documented format for other future research.
To the best of the authors’ knowledge, this research is the
first to address classifying poem meters in a machine learning approach, in general, and in RNN featureless based approach, in
particular. In addition, the dataset is the first publicly available
dataset ready for the purpose of future computational research.
Index Terms—Poetry, Meters, Al-’arud, Arabic, English, Recurrent Neural Networks, RNN, Deep Learning, Deep Neural Networks, DNN, Classification, Text Mining.