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Elayeb B.,Manouba University | Khiroun O.B.,Manouba University | Evrard F.,Informatics Research Institute of Toulouse IRIT | Bellamine-BenSaoud N.,Manouba University
International Journal of Intelligent Information Technologies | Year: 2011

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary "Le Grand Robert". First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure (possibility and necessity) between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard 'LeMonde94' test collection. Copyright © 2012, IGI Global.


Bounhas I.,Manouba University | Elayeb B.,Manouba University | Elayeb B.,P.A. College | Evrard F.,Informatics Research Institute of Toulouse IRIT | Slimani Y.,Manouba University
Journal of Computing and Cultural Heritage | Year: 2015

The literature on information retrieval shows the importance of information reliability as a key criterion for relevance judgment. However, information reliability evaluation is discussed in many disciplines such as history, Arabic storytelling, and computer science. Although these disciplines share common principles, they differ in many aspects, which are studied in this article. However, we mainly focus on two disciplines. On the one hand, Arabic storytelling stands by rigid rules for transmitting information and inspecting sources and contents. On the other hand, the characteristics of the Web as a collaborative, open and vast area for information sharing has caused changes in our evaluation of information. This article studies related works to enumerate the main principles and steps that constitute guidelines for automatic information reliability evaluation. Finally, these guidelines are applied to Arabic storytelling, and experimental results are presented. © 2015 ACM.


Elayeb B.,Manouba University | Elayeb B.,P.A. College | Bounhas I.,Manouba University | Ben Khiroun O.,Manouba University | And 2 more authors.
Knowledge and Information Systems | Year: 2015

This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems. © 2014, Springer-Verlag London.


Ayed R.,Manouba University | Bounhas I.,Manouba University | Elayeb B.,Manouba University | Elayeb B.,P.A. College | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Morphological ambiguity is an important problem that has been studied through different approaches. We investigate, in this paper, some classification methods to disambiguate Arabic morphological features of non-vocalized texts. A possibilistic approach is improved and proposed to handle imperfect training and test datasets. We introduce a data transformation method to convert the imperfect dataset to a perfect one. We compare the disambiguation results of classification approaches to results given by the possibilistic classifier dealing with imperfection context. © Springer International Publishing Switzerland 2014.


Ben Khiroun O.,Manouba University | Ayed R.,Manouba University | Elayeb B.,Manouba University | Elayeb B.,P.A. College | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

We propose in this paper a new standard Arabic test collection for mono- and cross-language Information Retrieval (CLIR). To do this, we exploit the "Hadith" texts and we provide a portal for sampling and evaluation of Hadiths' results listed in both Arabic and English versions. The new called "Kunuz" standard Arabic test collection will promote and restart the development of Arabic mono retrieval and CLIR systems blocked since the earlier TREC-2001 and TREC-2002 editions. © Springer International Publishing Switzerland 2014.


Elayeb B.,Manouba University | Elayeb B.,P.A. College | Bounhas I.,Manouba University | Evrard F.,Informatics Research Institute of Toulouse IRIT
Knowledge and Information Systems | Year: 2015

This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems. © 2014, Springer-Verlag London.


Khiroun O.B.,Manouba University | Elayeb B.,Manouba University | Elayeb B.,P.A. College | Bounhas I.,Manouba University | And 3 more authors.
ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence | Year: 2014

We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for monolingual intelligent information retrieval. The proposed approaches for WSD and QE are based on corpus analysis using co-occurrence graphs modelled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantages of a double measure (possibility and necessity). Our experiments are performed using the standard ROMANSEVAL test collection for the WSD task and the CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) evaluation. The results show the positive impact of WSD on QE based on the recall/precision standard metrics.


Bounhas I.,Manouba University | Ayed R.,Manouba University | Elayeb B.,Manouba University | Elayeb B.,P.A. College | And 3 more authors.
Computer Speech and Language | Year: 2015

In this paper, we experiment a discriminative possibilistic classifier with a reweighting model for morphological disambiguation of Arabic texts. The main idea is to provide a possibilistic classifier that acquires automatically disambiguation knowledge from vocalized corpora and tests on non-vocalized texts. Initially, we determine all the possible analyses of vocalized words using a morphological analyzer. The values of their morphological features are exploited to train the classifier. The testing phase consists in identifying the accurate class value (i.e., a morphological feature) using the features of the preceding and the following words. The appropriate class is the one having the greatest value of a possibilistic measure computed over the training set. To discriminate the effect of each feature, we add the weights of the training attributes to this measure. To assess this approach, we carry out experiments on a corpus of Arabic stories and on the Arabic Treebank. We present results concerning all the morphological features and we discern to which degree the discriminative approach improves disambiguation rates and extract the dependency relationships among the features. The results reveal the contribution of possibility theory for resolving ambiguities in real applications. We also compare the success rates in modern versus classical Arabic texts. Finally, we try to evaluate the impact of the lexical likelihood in morphological disambiguation. © 2014 Elsevier Ltd. All rights reserved.

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