Deep learning has emerged as a new area of machine research. It is an approach that can learn features and hierarchical representation purely from data been successfully applied to several fields such images, sounds, text motion. The techniques developed deep research have already impacting the on Natural Language Processing (NLP). Arabic diacritics are vital components remove ambiguity words r...

We extend the word2vec framework to capture meaning across languages. The input consists of a source text and a word-aligned parallel text in a second language. The joint word2vec tool then represents words in both languages within a common “semantic” vector space. The result can be used to enrich lexicons of under-resourced languages, to identify ambiguities, and to perform clustering and clas...

We present a limited speech translation system for English and colloquial Levantine Arabic, which we are currently developing as part of the DARPA Babylon program. The system is intended for question/answer communication between an English-speaking operator and an Arabic-speaking subject. It uses speech recognition to convert a spoken English question into text, and plays out a pre-recorded spe...

Building a system to extract Arabic named entities is a complex task due to the ambiguity and structure of Arabic text. Previous approaches that have tackled the problem of Arabic named entity recognition relied heavily on Arabic parsers and taggers combined with a huge set of gazetteers and sometimes large training sets to solve the ambiguity problem. But while these approaches are applicable ...

Text categorization refers to the process of grouping text or documents into classes categories according their content. consists three phases which are: preprocessing, feature extraction and classification. In comparison English language, just few studies have been done categorize classify Arabic language. For a variety applications, such as classification clustering, representation is difficu...

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature requires solution quality, stability, conver- gence speed, ability find global optimal. This study proposes method using Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic classification. HBGWO combines local search capabilities or exploratory BGWO around best solutions exploits ...

Arabic is known for its sparseness, which explains the difficulty of its automatic processing. The Arabic language is based on schemes; lemmas are produced using derivation based on roots and schemes. This latter character presents two major advantages: First, this “hidden side” of the Arabic language composed of schemes suffers much less from sparseness since it represents a finite set, second...

With the massive rise in the volume of information available on the World Wide Web these days, and the emergence requirements for a superior technique to access this information, there has been a strong resurgence of interest in web mining research. Web mining is a critical issue in data mining as well as other information process techniques to the World Wide Web to discover useful patterns. Pe...