Khaled Shaalan


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Extracting Synonyms from Bilingual Dictionaries
Mustafa Jarrar | Eman Naser | Muhammad Khalifa | Khaled Shaalan
Proceedings of the 11th Global Wordnet Conference

We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries. Identification and usage of synonyms play a significant role in improving the performance of information access applications. The idea is to construct a translation graph from translation pairs, then to extract and consolidate cyclic paths to form bilingual sets of synonyms. The initial evaluation of this algorithm illustrates promising results in extracting Arabic-English bilingual synonyms. In the evaluation, we first converted the synsets in the Arabic WordNet into translation pairs (i.e., losing word-sense memberships). Next, we applied our algorithm to rebuild these synsets. We compared the original and extracted synsets obtaining an F-Measure of 82.3% and 82.1% for Arabic and English synsets extraction, respectively.

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Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling
Muhammad Khalifa | Muhammad Abdul-Mageed | Khaled Shaalan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

A sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks. Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and dialects. We propose to self-train pre-trained language models in zero- and few-shot scenarios to improve performance on data-scarce varieties using only resources from data-rich ones. We demonstrate the utility of our approach in the context of Arabic sequence labeling by using a language model fine-tuned on Modern Standard Arabic (MSA) only to predict named entities (NE) and part-of-speech (POS) tags on several dialectal Arabic (DA) varieties. We show that self-training is indeed powerful, improving zero-shot MSA-to-DA transfer by as large as ˷10% F1 (NER) and 2% accuracy (POS tagging). We acquire even better performance in few-shot scenarios with limited amounts of labeled data. We conduct an ablation study and show that the performance boost observed directly results from training data augmentation possible with DA examples via self-training. This opens up opportunities for developing DA models exploiting only MSA resources. Our approach can also be extended to other languages and tasks.


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A Survey of Arabic Named Entity Recognition and Classification
Khaled Shaalan
Computational Linguistics, Volume 40, Issue 2 - June 2014


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Handling Unknown Words in Arabic FST Morphology
Khaled Shaalan | Mohammed Attia
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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The Floating Arabic Dictionary: An Automatic Method for Updating a Lexical Database through the Detection and Lemmatization of Unknown Words
Mohammed Attia | Younes Samih | Khaled Shaalan | Josef van Genabith
Proceedings of COLING 2012

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A Pipeline Arabic Named Entity Recognition using a Hybrid Approach
Mai Oudah | Khaled Shaalan
Proceedings of COLING 2012

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Improved Spelling Error Detection and Correction for Arabic
Mohammed Attia | Pavel Pecina | Younes Samih | Khaled Shaalan | Josef van Genabith
Proceedings of COLING 2012: Posters

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Arabic Word Generation and Modelling for Spell Checking
Khaled Shaalan | Mohammed Attia | Pavel Pecina | Younes Samih | Josef van Genabith
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Arabic is a language known for its rich and complex morphology. Although many research projects have focused on the problem of Arabic morphological analysis using different techniques and approaches, very few have addressed the issue of generation of fully inflected words for the purpose of text authoring. Available open-source spell checking resources for Arabic are too small and inadequate. Ayaspell, for example, the official resource used with OpenOffice applications, contains only 300,000 fully inflected words. We try to bridge this critical gap by creating an adequate, open-source and large-coverage word list for Arabic containing 9,000,000 fully inflected surface words. Furthermore, from a large list of valid forms and invalid forms we create a character-based tri-gram language model to approximate knowledge about permissible character clusters in Arabic, creating a novel method for detecting spelling errors. Testing of this language model gives a precision of 98.2% at a recall of 100%. We take our research a step further by creating a context-independent spelling correction tool using a finite-state automaton that measures the edit distance between input words and candidate corrections, the Noisy Channel Model, and knowledge-based rules. Our system performs significantly better than Hunspell in choosing the best solution, but it is still below the MS Spell Checker.

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Automatic Extraction and Evaluation of Arabic LFG Resources
Mohammed Attia | Khaled Shaalan | Lamia Tounsi | Josef van Genabith
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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.


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Adaptive Feedback Message Generation for Second Language Learners of Arabic
Khaled Shaalan | Marwa Magdy
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011


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Syntactic Generation of Arabic in Interlingua-based Machine Translation Framework
Khaled Shaalan | Azza Abdel Monem | Ahmed Rafea
Proceedings of the Third Workshop on Computational Approaches to Arabic-Script-based Languages (CAASL3)

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A Hybrid Approach for Building Arabic Diacritizer
Khaled Shaalan | Hitham M. Abo Bakr | Ibrahim Ziedan
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages


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Person Name Entity Recognition for Arabic
Khaled Shaalan | Hafsa Raza
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources


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Mapping Interlingua Representations to Feature Structures of Arabic Sentences
Khaled Shaalan | Azza Abdel Monem | Ahmed Rafea | Hoda Baraka
Proceedings of the International Conference on the Challenge of Arabic for NLP/MT

The interlingua approach to Machine Translation (MT) aims to achieve the translation task in two independent steps. First, the meanings of source language sentences are represented in an intermediate (interlingua) representation. Then, sentences of the target language are generated from those meaning representations. In the generation of the target sentence, determining sentence structures becomes more difficult, especially when the interlingua does not contain any syntactic information. Hence, the sentence structures cannot be transferred exactly from the interlingua representations. In this paper, we present a mapping approach for task- oriented interlingua-based spoken dialogue that transforms an interlingua representation, so-called Interchange Format (IF), into a feature structure (FS) that reflects the syntactic structure of the target Arabic sentence. This approach addresses the handling of the problem of Arabic syntactic structure determination in the interlingua approach. A mapper is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic FS mapper is implemented in SICStus Prolog. Examples of Arabic syntactic mapping, using the output from the English analyzer provided by Carnegie Mellon University (CMU), will illustrate how the system works.


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A chart parser for analyzing modern standard Arabic sentence
Eman Othman | Khaled Shaalan | Ahmed Rafea
Workshop on Machine Translation for Semitic languages: issues and approaches

The parsing of Arabic sentence is a necessary prerequisite for many natural language processing applications such as machine translation and information retrieval. In this paper we report our attempt to develop an efficient chart parser for Analyzing Modern Standard Arabic (MSA) sentence. From a practical point of view, the parser is able to satisfy syntactic constraints reducing parsing ambiguity. Lexical semantic features are also used to disambiguate the sentence structure. We explain also an Arabic morphological analyzer based on ATN technique. Both the Arabic parser and the Arabic morphological analyzer are implemented in Prolog. The linguistic rules were acquired from a set of sentences from MSA sentence in the Agriculture domain.