Laroussi Merhbene

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In this paper, we propose to use Harman, Croft and Okapi measures with Lesk algorithm to develop a system for Arabic word sense disambiguation, that combines unsupervised and knowledge based methods. This system must solve the lexical semantic ambiguity in Arabic language. The information retrieval measures are used to estimate the most relevant sense of(More)
In this paper we put forward an unsupervised system WSD-AL for Arabic word disambiguation. We apply some pre-processing steps to texts containing the ambiguous word in the corpus and we extract the most relevant words. Then, we put to use the Context-Matching algorithm that returns a semantic coherence score corresponding to the context of use that is(More)
In this paper, we evaluate the variants of the Lesk algorithm to disambiguate Arabic words. In the first experiment we apply the original Lesk algorithm using the dictionary as a resource. As a second experience, we add some modifications for this algorithm, using the different similarity measures to determine the similarity relatedness between two concepts(More)
this paper we test some supervised algorithms that most of the existing related works of word sense disambiguation have cited. Due to the lack of linguistic data for the Arabic language, we work on non-annotated corpus and with the help of four annotators; we were able to annotate the different samples containing the ambiguous words. Since that, we test the(More)
In this paper, we present a hybrid approach for Word Sense Disambiguation of Arabic Language (called WSD-AL), that combines unsupervised and knowledge-based methods. Some pre-processing steps are applied to texts containing the ambiguous words in the corpus (1500 texts extracted from the web), and the salient words that affect the meaning of these words are(More)
In this paper we propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the most closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular(More)
In this paper, we propose a new semi-supervised approach for Arabic word sense disambiguation. Using the corpus and Arabic Wordnet 1 , we define a method to cluster the sentences containing ambiguous words. For each sense, we generate a cluster that we use to construct a semantic tree. Furthermore, we construct a weighted directed graph by matching the tree(More)
The problem of word sense disambiguation is one of the oldest problems of natural language processing. In this paper, we propose a semi-supervised approach to word sense disambiguation. The Supervised part of our method uses the corpus and the dictionary as a resource to classify the contexts of the ambiguous word by sense. The combination of these contexts(More)
Laroussi Merhben UTIC(Monastir unit) higher school of techniques sciences of Tunis. Abstract In this paper we propose an hybrid system of Arabic words disambiguation. To achieve this goal we use the methods employed in the domain of information retrieval: Latent semantic analysis, Harman, Croft, Okapi, combined to the lesk algorithm. These methods are used(More)