Papers by Mohammad Salameh
The MADAR Arabic Dialect Corpus and Lexicon (L18-1)
Copied to clipboard
Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alexander Erdmann, Kemal Oflazer
| Challenge: | Using a corpus of 25 Arabic city dialects and a lexicon of 1,045 concepts, we study 25 cities in a travel domain . focus on cities opens new avenues for research from dialectology to dialect identification and machine translation. |
| Approach: | They present two Arabic language resources that are part of the Multi Arabic Dialect Applications and Resources project. |
| Outcome: | The proposed resources are the first of their kind in terms of their coverage and fine granularity. |
Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
Copied to clipboard
Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, Hind Saddiki
| Challenge: | Existing efforts to conventionalize the dialectal orthography of Arabic have focused on specific dialects and made ad hoc decisions. |
| Approach: | They propose a set of guidelines and meta-guidelines for conventional orthography of Arabic dialects . they apply them to 28 Arab city dialects from Rabat to Muscat . |
| Outcome: | The proposed guidelines and resources are being used by three large Arabic dialect processing projects in three universities. |
Fine-Grained Arabic Dialect Identification (C18-1)
Copied to clipboard
| Challenge: | Existing work on Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic at most (6-way classification). |
| Approach: | They propose to tackle a fine-grained Arabic dialect classification task covering 25 cities from across the Arab World, in addition to Standard Arabic. |
| Outcome: | The proposed task can identify the exact city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words and reach more than 90% when we consider 16 words. |
ADIDA: Automatic Dialect Identification for Arabic (N19-4)
Copied to clipboard
| Challenge: | Demo paper describes a web-based system for automatic dialect identification for Arabic text. |
| Approach: | They present a web-based system for automatic dialect identification for Arabic text that distinguishes between 25 Arab cities and Modern Standard Arabic. |
| Outcome: | The proposed system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) and Modern Standard Arabic (MSA). |