Khaled Shaalan

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, non-standardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.}

Arabic is a language of rich and complex morphology. The nature and peculiarity of Arabic make its morphological and phonological rules confusing for second language learners (SLLs). The conjugation of Arabic verbs is central to the formulation of an Arabic sentence because of its richness of form and meaning. In this paper, we address issues related to the morphological analysis of ill-formed Arabic verbs in order to identify the source of errors and provide an in-formative feedback to SLLs of Arabic. The edit distance and constraint relaxation techniques are used to demonstrate the capability of the proposed approach in generating all possible analyses of erroneous Arabic verbs written by SLLs. Filtering mechanisms are applied to exclude the irrelevant constructions and determine the target stem. A morphological analyzer has been developed and effectively evaluated using real test data. It achieved satisfactory results in terms of the recall rate.

Ambiguity is a major issue in any NLP application that occurs when multiple interpretations of the same language phenomenon are produced. Given the complexity of the Arabic morphological system, it is difficult to determine what the intended meaning of the writer is. Moreover, Intelligent Language Tutoring Systems which need to analyze erroneous learner answers, generally, introduce techniques, such as constraints relaxation, that would produce more interpretations than systems designed for processing well-formed input. This paper addresses issues related to the morphological disambiguation of corrected interpretations of erroneous Arabic verbs that were written by beginner to intermediate Second Language Learners. The morphological disambiguation has been developed and effectively evaluated using real test data. It achieved satisfactory results in terms of the recall rate.