no code implementations • 8 Jun 2020 • Mohammed Hossny, Khaled Saleh, Mohammed Attia, Ahmed Abobakr, Julie Iskander
In this paper, we present a novel method to simulate LiDAR point cloud with faster rendering time of 1 sec per frame.
no code implementations • 4 Jun 2020 • Mohammed Hossny, Julie Iskander, Mohammed Attia, Khaled Saleh
In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load conditions during interaction with the environment.
no code implementations • WS 2019 • Mohammed Attia, Ali Elkahky
Segmentation serves as an integral part in many NLP applications including Machine Translation, Parsing, and Information Retrieval.
no code implementations • WS 2019 • Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Mohammed Attia, Mohamed Eldesouki, Kareem Darwish
This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification).
no code implementations • WS 2019 • Mohammed Attia, Younes Samih, Ali Elkahky, Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish
When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language.
no code implementations • 22 May 2019 • Khaled Saleh, Ahmed Abobakr, Mohammed Attia, Julie Iskander, Darius Nahavandi, Mohammed Hossny
We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors.
Ranked #2 on Unsupervised Domain Adaptation on PreSIL to KITTI
no code implementations • 15 Oct 2018 • Ahmed Abdelali, Mohammed Attia, Younes Samih, Kareem Darwish, Hamdy Mubarak
Diacritization process attempt to restore the short vowels in Arabic written text; which typically are omitted.
no code implementations • WS 2018 • Mohammed Attia, Younes Samih, Wolfgang Maier
This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic.
no code implementations • SEMEVAL 2018 • Mohammed Attia, Younes Samih, Manaal Faruqui, Wolfgang Maier
This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes.
2 code implementations • 19 Aug 2017 • Mohamed Eldesouki, Younes Samih, Ahmed Abdelali, Mohammed Attia, Hamdy Mubarak, Kareem Darwish, Kallmeyer Laura
Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval.
Ranked #1 on Sentiment Analysis on DynaSent (using extra training data)
no code implementations • CONLL 2017 • Younes Samih, Mohamed Eldesouki, Mohammed Attia, Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer
Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other.
no code implementations • CONLL 2017 • Daniel Zeman, Martin Popel, Milan Straka, Jan Haji{\v{c}}, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkov{\'a}, Jan Haji{\v{c}} jr., Jaroslava Hlav{\'a}{\v{c}}ov{\'a}, V{\'a}clava Kettnerov{\'a}, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Jenna Kanerva, Stina Ojala, Anna Missil{\"a}, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria de Paiva, Kira Droganova, H{\'e}ctor Mart{\'\i}nez Alonso, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, M, Michael l, Jesse Kirchner, Hector Fern Alcalde, ez, Jana Strnadov{\'a}, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendon{\c{c}}a, L, Tatiana o, Rattima Nitisaroj, Josie Li
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
no code implementations • WS 2017 • Younes Samih, Mohammed Attia, Mohamed Eldesouki, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer, Kareem Darwish
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.
no code implementations • WS 2016 • Mohammed Attia, Ayah Zirikly, Mona Diab
The interaction between roots and patterns in Arabic has intrigued lexicographers and morphologists for centuries.
no code implementations • WS 2016 • Mohammed Attia, Suraj Maharjan, Younes Samih, Laura Kallmeyer, Thamar Solorio
The evaluation results of our system on the test set is 88. 1{\%} (79. 0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76. 0{\%} (42. 3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations.
no code implementations • LREC 2016 • Abdelati Hawwari, Mohammed Attia, Mahmoud Ghoneim, Mona Diab
Identifying the various types of the Idafa construction (IC) is of importance to Natural Language processing (NLP) applications.
no code implementations • LREC 2014 • Mona Diab, Mohamed Al-Badrashiny, Maryam Aminian, Mohammed Attia, Heba Elfardy, Nizar Habash, Abdelati Hawwari, Wael Salloum, Pradeep Dasigi, Esk, Ramy er
Multiple levels of quality checks are performed on the output of each step in the creation process.
no code implementations • LREC 2012 • Mohammed Attia, Khaled Shaalan, Lamia Tounsi, Josef van Genabith
We utilize this annotation to automatically acquire grammatical function (dependency) based subcategorization frames and paths linking long-distance dependencies (LDDs).
no code implementations • LREC 2012 • Khaled Shaalan, Mohammed Attia, Pavel Pecina, Younes Samih, Josef van Genabith
Furthermore, from a large list of valid forms and invalid forms we create a character-based tri-gram language model to approximate knowledge about permissible character clusters in Arabic, creating a novel method for detecting spelling errors.