A Neural Architecture for Dialectal Arabic Segmentation
Younes Samih, Mohammed Attia, Mohamed Eldesouki, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer, Kareem Darwish
Abstract
The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.- Anthology ID:
- W17-1306
- Volume:
- Proceedings of the Third Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Nizar Habash, Mona Diab, Kareem Darwish, Wassim El-Hajj, Hend Al-Khalifa, Houda Bouamor, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- SEMITIC
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–54
- Language:
- URL:
- https://aclanthology.org/W17-1306
- DOI:
- 10.18653/v1/W17-1306
- Cite (ACL):
- Younes Samih, Mohammed Attia, Mohamed Eldesouki, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer, and Kareem Darwish. 2017. A Neural Architecture for Dialectal Arabic Segmentation. In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 46–54, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A Neural Architecture for Dialectal Arabic Segmentation (Samih et al., WANLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W17-1306.pdf
- Data
- Egyptian Arabic Segmentation Dataset