Developing natural language processing tools usually requires a large number of resources (lexica, annotated corpora, etc.), which often do not exist for less-resourced languages. One way to overcome the problem of lack of resources is to devote substantial efforts to build new ones from scratch. Another approach is to exploit existing resources of closely related languages. In this paper, we f...

We develop an open-source large-scale finitestate morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license.1 The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary ...

on this paper, we proposed a new text line segmentation of handwritten and typewriting Arabic document images that uses the Outer Isothetic Cover (OIC) algorithm of a digital object. In the first step, we use this method to segment the composed document into text blocs. In the second step, for each text bloc we will extract the text lines. Finally, line text will be segmented into words or into...

The absence of the vowelization marks from the modern Arabic text represents a major obstacle in machine translation and other text understanding applications. In this paper we present a formulation of the problem of automatic generation of the Arabic diacritic marks from unvoweled text using a Hidden Markov Model (HMM) approach. The model considers the word sequence of unvoweled Arabic text as...

Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval. Segmentation entails breaking words into their constituent stems, affixes and clitics. In this paper, we compare two approaches for segmenting four major Arabic dialects using only several thousand training examples for each dialect. The two approaches involve posing th...

This paper describes recent research on transcribing Modern Standard Arabic broadcast news data. The Arabic language presents a number of challenges for speech recognition, arising in part from the significant differences in the spoken and written forms, in particular the conventional form of texts being non-vowelized. Arabic is a highly inflected language where articles and affixes are added t...

Useful training data for automatic speech recognition systems of colloquial speech is usually limited to expensive in-domain transcription. Broadcast news is an appealing source of easily available data to bootstrap into a new dialect. However, some languages, like Arabic, have deep linguistic differences resulting in poor cross domain performance. If no in-domain transcripts are available, but...

We exploit the resources in the Arabic Treebank (ATB) and Arabic Gigaword (AG) to determine the best features for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). The verbs are classified into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. The results of the clustering experiments are compared wi...

The paper presents work on improved sentence-level dialect classification of Egyptian Arabic (ARZ) vs. Modern Standard Arabic (MSA). Our approach is based on binary feature functions that can be implemented with a minimal amount of task-specific knowledge. We train a featurerich linear classifier based on a linear support-vector machine (linear SVM) approach. Our best system achieves an accurac...

The majority of research on Arabic Named Entity Recognition (NER) addresses the the task for newswire genre, where the language used is Modern Standard Arabic (MSA), however, the need to study this task in social media is becoming more vital. Social media is characterized by the use of both MSA and Dialectal Arabic (DA), with often code switching between the two language varieties. Despite some...