Ahmed Rafea


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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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Preprocessing Egyptian Dialect Tweets for Sentiment Mining
Amira Shoukry | Ahmed Rafea
Fourth Workshop on Computational Approaches to Arabic-Script-based Languages

Research done on Arabic sentiment analysis is considered very limited almost in its early steps compared to other languages like English whether at document-level or sentence-level. In this paper, we test the effect of preprocessing (normalization, stemming, and stop words removal) on the performance of an Arabic sentiment analysis system using Arabic tweets from twitter. The sentiment (positive or negative) of the crawled tweets is analyzed to interpret the attitude of the public with regards to topic of interest. Using Twitter as the main source of data reflects the importance of the system for the Middle East region, which mostly speaks Arabic.


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KP-Miner: Participation in SemEval-2
Samhaa R. El-Beltagy | Ahmed Rafea
Proceedings of the 5th International Workshop on Semantic Evaluation


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Syntactic Generation of Arabic in Interlingua-based Machine Translation Framework
Khaled Shaalan | Azza Abdel Monem | Ahmed Rafea
Proceedings of the Third Workshop on Computational Approaches to Arabic-Script-based Languages (CAASL3)


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Mapping Interlingua Representations to Feature Structures of Arabic Sentences
Khaled Shaalan | Azza Abdel Monem | Ahmed Rafea | Hoda Baraka
Proceedings of the International Conference on the Challenge of Arabic for NLP/MT

The interlingua approach to Machine Translation (MT) aims to achieve the translation task in two independent steps. First, the meanings of source language sentences are represented in an intermediate (interlingua) representation. Then, sentences of the target language are generated from those meaning representations. In the generation of the target sentence, determining sentence structures becomes more difficult, especially when the interlingua does not contain any syntactic information. Hence, the sentence structures cannot be transferred exactly from the interlingua representations. In this paper, we present a mapping approach for task- oriented interlingua-based spoken dialogue that transforms an interlingua representation, so-called Interchange Format (IF), into a feature structure (FS) that reflects the syntactic structure of the target Arabic sentence. This approach addresses the handling of the problem of Arabic syntactic structure determination in the interlingua approach. A mapper is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic FS mapper is implemented in SICStus Prolog. Examples of Arabic syntactic mapping, using the output from the English analyzer provided by Carnegie Mellon University (CMU), will illustrate how the system works.


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A chart parser for analyzing modern standard Arabic sentence
Eman Othman | Khaled Shaalan | Ahmed Rafea
Workshop on Machine Translation for Semitic languages: issues and approaches

The parsing of Arabic sentence is a necessary prerequisite for many natural language processing applications such as machine translation and information retrieval. In this paper we report our attempt to develop an efficient chart parser for Analyzing Modern Standard Arabic (MSA) sentence. From a practical point of view, the parser is able to satisfy syntactic constraints reducing parsing ambiguity. Lexical semantic features are also used to disambiguate the sentence structure. We explain also an Arabic morphological analyzer based on ATN technique. Both the Arabic parser and the Arabic morphological analyzer are implemented in Prolog. The linguistic rules were acquired from a set of sentences from MSA sentence in the Agriculture domain.