Named entity recognition is an involved task and is one that usually requires the usage of numerous resources. Recognizing Arabic entities is an even more difficult task due to the inherent ambiguity of the Arabic language. Previous approaches that have tackled the problem of Arabic named entity recognition have used Arabic parsers and taggers combined with a huge set of gazetteers and sometime...

Since the advent of the Semantic Web in the late 90’s, many Web applications were created to benefit from the capabilities provided by Semantic Web technologies. These capabilities include intelligent reasoning over data, semantic search and data interoperability. However, most Semantic Web technologies are dedicated to processing Latin family scripts, thus, an apparent lack of Arabic script su...

We address the problem of Part of Speech tagging (POS) in the context of linguistic code switching (CS). CS is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential CS, respectively. Processing CS data is especially challenging in intrasentential data given state of the art monoli...

word formation and word selection in one hand’ are considered from higher processes a word aqualization which has a wide range application for language theorists and researchers in order to enrich language or in a better way in order to set free them from words. inadequacy challenge and also lach of the knowledge of the words in technology and industry fields aspecially in arabic and persian sp...

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs ...

We present a working Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, organization, location, date and number, as well as quotations (direct reported speech) by and about people. The Named Entity Recognition (NER) system was not developed for Arabic, but instead a highly multilingual, almost la...

Sentiment Analysis is a very challenging and important task that contains natural language processing, web mining and machine learning. Up to date, few researches have been conducted on sentiment classification for Arabic languages due to the lack of resources for managing sentiments or opinions such as senti-lexicons and opinion corpora. The main obstacle in Arabic sentiment analysis is that p...

Here we describe a work-in-progress approach for learning valencies of verbs in a morphologically rich language using only a morphological analyzer and an unannotated corpus. We will compare the results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same text in treebank form. The approach will then be applied to an unannotated corpus from Quec...