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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus
AU - El-Haj, Mahmoud
PY - 2020/5/11
Y1 - 2020/5/11
N2 - This paper introduces Habibi the first Arabic Song Lyrics corpus. The corpus comprises more than 30,000 Arabic song lyrics in 6Arabic dialects for singers from 18 different Arabic countries. The lyrics are segmented into more than 500,000 sentences (song verses)with more than 3.5 million words. I provide the corpus in both comma separated value (csv) and annotated plain text (txt) file formats.In addition, I converted the csv version into JavaScript Object Notation (json) and eXtensible Markup Language (xml) file formats.To experiment with the corpus I run extensive binary and multi-class experiments for dialect and country-of-origin identification. Theidentification tasks include the use of several classical machine learning and deep learning models utilising different word embeddings.For the binary dialect identification task the best performing classifier achieved a testing accuracy of 93%. This was achieved using aword-based Convolutional Neural Network (CNN) utilising a Continuous Bag of Words (CBOW) word embeddings model. The resultsoverall show all classical and deep learning models to outperform our baseline, which demonstrates the suitability of the corpus for bothdialect and country-of-origin identification tasks. I am making the corpus and the trained CBOW word embeddings freely available forresearch purposes.
AB - This paper introduces Habibi the first Arabic Song Lyrics corpus. The corpus comprises more than 30,000 Arabic song lyrics in 6Arabic dialects for singers from 18 different Arabic countries. The lyrics are segmented into more than 500,000 sentences (song verses)with more than 3.5 million words. I provide the corpus in both comma separated value (csv) and annotated plain text (txt) file formats.In addition, I converted the csv version into JavaScript Object Notation (json) and eXtensible Markup Language (xml) file formats.To experiment with the corpus I run extensive binary and multi-class experiments for dialect and country-of-origin identification. Theidentification tasks include the use of several classical machine learning and deep learning models utilising different word embeddings.For the binary dialect identification task the best performing classifier achieved a testing accuracy of 93%. This was achieved using aword-based Convolutional Neural Network (CNN) utilising a Continuous Bag of Words (CBOW) word embeddings model. The resultsoverall show all classical and deep learning models to outperform our baseline, which demonstrates the suitability of the corpus for bothdialect and country-of-origin identification tasks. I am making the corpus and the trained CBOW word embeddings freely available forresearch purposes.
M3 - Conference contribution/Paper
BT - LREC 2020, Twelfth International Conference on Language Resources and Evaluation
PB - European Language Resources Association (ELRA)
T2 - The 12th Edition of the Language Resources and Evaluation Conference (LREC2020)
Y2 - 11 May 2020 through 16 May 2020
ER -