Ambiguous Arabic Words Disambiguation

Merhbène, Laroussi; Zouaghi, Anis; Zrigui, Mounir

doi:10.1109/snpd.2010.32

Cited by 19 publications

(7 citation statements)

References 9 publications

(21 reference statements)

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“…The result of these methods cannot be compared directly to our algorithms' results because those methods are for different tasks and their results were generated using different datasets. For that reason, in this comparison, we apply the same experimental data that we have used before in the experimental study of the Genetic algorithm (Menai et al, 2012), Naïve Bayes classifier (Elmougy et al, 2008) and Modified version of the Lesk (Merhben et al, 2009) Methods Score Ant colony algorithm (our approach) 80 % Genetic algorithm 78.9 % Naïve Bayes classifier 76.6 % Modified version of the Lesk 67% The results obtained with the ant colony algorithm corroborate those obtained in previous studies on ACO for WSD in English (Schwab et al, 2011(Schwab et al, , 2012(Schwab et al, , and 2013 even though they are not comparable.…”

Section: E Comparison With Other Methodsmentioning

confidence: 99%

Ant colony algorithm for Arabic word sense disambiguation through English lexical information

Bakhouche

Tlili

Schwab

et al. 2015

IJMSO

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The ability to identify the intended meanings of words in context is a central research topic in natural language. Many solutions exist for word sense disambiguation (WSD) in different languages, such as English or French, but research on Arabic WSD remains limited. The main bottleneck is the lack of resources. In this article, we show that it is possible to build a WSD system for the Arabic language thanks to the Arabic WordNet and its connexions to the English Princeton WordNet. Given that the Arabic WordNet does not contain definitions for synsets, we construct a dictionary that maps the Princeton WordNet definitions to the Arabic WordNet. We also create an Arabic evaluation corpus and gold standard. We then exploit this dictionary and evaluation corpus to run and evaluate an adapted Ant Colony algorithm on Arabic text that can use the Lesk similarity measure thanks to definition mapping. The algorithm shows a performance of approximately 80% compared to the random baseline of 78.9 %.

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Section: E Comparison With Other Methodsmentioning

confidence: 99%

Ant colony algorithm for Arabic word sense disambiguation through English lexical information

Bakhouche

Tlili

Schwab

et al. 2015

IJMSO

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“…However, there are some works applied to Arabic. We can state the unsupervised approach of Bootstrapping Arabic Sense Tagging [15], the naïve Bayes classifier for AWSD [16], the Arabic WSD by using the variants of Lesk algorithm [17], the WSD-AL system [18,19], and so forth. Here, we define an unsupervised method named.…”

Section: Proposed Methodsmentioning

confidence: 99%

Contribution to Semantic Analysis of Arabic Language

Zouaghi

Zrigui

Antoniadis

et al. 2012

Advances in Artificial Intelligence

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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 closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed.

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“…Merhben et al [27] have proposed a hybrid approach for WSD in Arabic language. In this experiment, the authors use Latent Semantic Analysis, Harman, Croft and Okapi methods for information retrieval, and finally, the Lesk algorithm is developed for sense disambiguation.…”

Section: 2hmentioning

confidence: 99%

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

Pal

Saha

2019

Sādhanā

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In this work, Word Sense Disambiguation (WSD) in Bengali language is implemented using unsupervised methodology. In the first phase of this experiment, sentence clustering is performed using Maximum Entropy method and the clusters are labelled with their innate senses by manual intervention, as these sense-tagged clusters could be used as sense inventories for further experiment. In the next phase, when a test data comes to be disambiguated, the Cosine Similarity Measure is used to find the closeness of that test data with the initially sense-tagged clusters. The minimum distance of that test data from a particular sense-tagged cluster assigns the same sense to the test data as that of the cluster it is assigned with. This strategy is considered as the baseline strategy, which produces 35% accurate result in WSD task. Next, two extensions are adopted over this baseline strategy: (a) Principal Component Analysis (PCA) over the feature vector, which produces 52% accuracy in WSD task and (b) Context Expansion of the sentences using Bengali WordNet coupled with PCA, which produces 61% accuracy in WSD task. The data sets that are used in this work are obtained from the Bengali corpus, developed under the Technology Development for the Indian Languages (TDIL) project of the Government of India, and the lexical knowledge base (i.e., the Bengali WordNet) used in the work is developed at the Indian Statistical Institute, Kolkata, under the Indradhanush Project of the DeitY, Government of India. The challenges and the pitfalls of this work are also described in detail in the pre-conclusion section.

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Ambiguous Arabic Words Disambiguation

Cited by 19 publications

References 9 publications

Ant colony algorithm for Arabic word sense disambiguation through English lexical information

Ant colony algorithm for Arabic word sense disambiguation through English lexical information

Contribution to Semantic Analysis of Arabic Language

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

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