Name: APCD
Description: A dataset of Arabic poetry containing 1,831,770 along with there meters. 
Provider: Helwan University
Paper Title: Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis
Tasks: meter classification
Venue Name: nan
Authors: Waleed A. Yousefa,Omar M. Ibrahime,Taha M. Madboulya, Moustafa A. Mahmoud
Abstract: 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.