Khaled Shaalan

This paper presents the results of an approach to automatically acquire large-scale, probabilistic Lexical-Functional Grammar (LFG) resources for Arabic from the Penn Arabic Treebank (ATB). Our starting point is the earlier, work of (Tounsi et al., 2009) on automatic LFG f(eature)-structure annotation for Arabic using the ATB. They exploit tree configuration, POS categories, functional tags, local heads and trace information to annotate nodes with LFG feature-structure equations. We utilize this annotation to automatically acquire grammatical function (dependency) based subcategorization frames and paths linking long-distance dependencies (LDDs). Many state-of-the-art treebank-based probabilistic parsing approaches are scalable and robust but often also shallow: they do not capture LDDs and represent only local information. Subcategorization frames and LDD paths can be used to recover LDDs from such parser output to capture deep linguistic information. Automatic acquisition of language resources from existing treebanks saves time and effort involved in creating such resources by hand. Moreover, data-driven automatic acquisition naturally associates probabilistic information with subcategorization frames and LDD paths. Finally, based on the statistical distribution of LDD path types, we propose empirical bounds on traditional regular expression based functional uncertainty equations used to handle LDDs in LFG.

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products. Many researchers have attacked this problem in a variety of languages but only a few limited researches have focused on Named Entity Recognition (NER) for Arabic text 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 paper, we present the results of our attempt at the recognition and extraction of 10 most important named entities in Arabic script; the person name, location, company, date, time, price, measurement, phone number, ISBN and file name. We developed the system, Name Entity Recognition for Arabic (NERA), using a rule-based approach. The system consists of a whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. NERA is evaluated using our own corpora that are tagged in a semi-automated way, and the performance results achieved were satisfactory in terms of precision, recall, and f-measure.

This paper addresses the development of an automated lexical error diagnosis system, which helps Arabic second language learners to learn well-formed weak verbs. The learners are encouraged to produce input freely in various situations and contexts and guided to recognize by themselves the erroneous or inappropriate functions of their misused expressions. In this system, we successfully used constraint relaxation and edit-distance techniques to provide error-specific diagnosis and feedback to second language learners of Arabic. We demonstrated the capabilities of these techniques to diagnose errors related to the Arabic weak verb which is formed using complex morphological rules. Furthermore, the developed system allows for individualization of the learning process by providing feedback that conforms to the learner’s expertise. Inexperienced learners might require detailed instruction while experienced learners benefit from higher level reminders and explanations.

Building NLP applications from scratch is a difficult task that takes a lot of time and requires acquiring a lot of NLP knowledge. For a rich language like Arabic the difficulties is increased significantly. In this paper, we investigated how to build a tool that helps NLP application developers to build rapid and robust applications. It involves two steps. Firstly, using COM objects technology in building common NLP tools. Secondly, building NLP applications that uses these tools which can access these tools either locally or remotely. We have demonstrated the capabilities of the COM objects in developing NLP tools such as morphological analyzer and used it for building two Arabic NLP applications.