Mohsen Rashwan

Also published as: M. Rashwan


2017

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An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems
Hany Ahmed | Mohamed Elaraby | Abdullah M. Mousa | Mostafa Elhosiny | Sherif Abdou | Mohsen Rashwan
Proceedings of the Third Arabic Natural Language Processing Workshop

In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network(SOM).The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to different speakers. In our experiments, we compared our technique with Normalized Cross Likelihood ratio Clustering (NCLR). Results show that the proposed technique reduces the speaker error rate in comparison with NCLR. Finally, we have experimented the effect of speaker clustering on Speaker Adaptive Training (SAT) in a speech recognition system implemented to test the performance of the proposed technique. It was noted that the proposed technique reduced the WER over clustering speakers with NCLR.

2016

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RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking
Ahmed Magooda | Amr Gomaa | Ashraf Mahgoub | Hany Ahmed | Mohsen Rashwan | Hazem Raafat | Eslam Kamal | Ahmad Al Sallab
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2014

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Automatic Arabic diacritics restoration based on deep nets
Ahmad Al Sallab | Mohsen Rashwan | Hazem M. Raafat | Ahmed Rafea
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Semantic Query Expansion for Arabic Information Retrieval
Ashraf Mahgoub | Mohsen Rashwan | Hazem Raafat | Mohamed Zahran | Magda Fayek
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

2008

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A Compact Arabic Lexical Semantics Language Resource Based on the Theory of Semantic Fields
Mohamed Attia | Mohsen Rashwan | Ahmed Ragheb | Mohamed Al-Badrashiny | Husein Al-Basoumy
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Applications of statistical Arabic NLP in general, and text mining in specific, along with the tools underneath perform much better as the statistical processing operates on deeper language factorization(s) than on raw text. Lexical semantic factorization is very important in that aspect due to its feasibility, high level of abstraction, and the language independence of its output. In the core of such a factorization lies an Arabic lexical semantic DB. While building this LR, we had to go beyond the conventional exclusive collection of words from dictionaries and thesauri that cannot alone produce a satisfactory coverage of this highly inflective and derivative language. This paper is hence devoted to the design and implementation of an Arabic lexical semantics LR that enables the retrieval of the possible senses of any given Arabic word at a high coverage. Instead of tying full Arabic words to their possible senses, our LR flexibly relates morphologically and PoS-tags constrained Arabic lexical compounds to a predefined limited set of semantic fields across which the standard semantic relations are defined. With the aid of the same large-scale Arabic morphological analyzer and PoS tagger in the runtime, the possible senses of virtually any given Arabic word are retrievable.

2006

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Building Annotated Written and Spoken Arabic LRs in NEMLAR Project
M. Yaseen | M. Attia | B. Maegaard | K. Choukri | N. Paulsson | S. Haamid | S. Krauwer | C. Bendahman | H. Fersøe | M. Rashwan | B. Haddad | C. Mukbel | A. Mouradi | A. Al-Kufaishi | M. Shahin | N. Chenfour | A. Ragheb
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The NEMLAR project: Network for Euro-Mediterranean LAnguage Resource and human language technology development and support (www.nemlar.org) was a project supported by the EC with partners from Europe and Arabic countries, whose objective is to build a network of specialized partners to promote and support the development of Arabic Language Resources (LRs) in the Mediterranean region. The project focused on identifying the state of the art of LRs in the region, assessing priority requirements through consultations with language industry and communication players, and establishing a protocol for developing and identifying a Basic Language Resource Kit (BLARK) for Arabic, and to assess first priority requirements. The BLARK is defined as the minimal set of language resources that is necessary to do any pre-competitive research and education, in addition to the development of crucial components for any future NLP industry. Following the identification of high priority resources the NEMLAR partners agreed to focus on, and produce three main resources, which are 1) Annotated Arabic written corpus of about 500 K words, 2) Arabic speech corpus for TTS applications of 2x5 hours, and 3) Arabic broadcast news speech corpus of 40 hours Modern Standard Arabic. For each of the resources underlying linguistic models and assumptions of the corpus, technical specifications, methodologies for the collection and building of the resources, validation and verification mechanisms were put and applied for the three LRs.