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... information is expressed by the symbols attached to each node in the input tape. Figure 5, demonstrates the availability of syntactic dependencies needed to disambiguate "."آارث The engagement of the verb took in "agt" and "obj" relationships, provides information to the enconverter to perform the correct segmentation and word selection. ...
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... In related literature, we find approaches based on heuristics, contextual and non-contextual rules (cf. Daoud, 2009;Daoud and Daoud, 2009;Othman et al., 2004 for more reading). For example, Daoud (2009) and Daoud and Daoud (2009) proposed to use Universal Networking Language (UNL) and EnCo 2 ; a rule-based programming language specialized for the writing of EnConverters (i.e., parsers) to define disambiguation rules. ...
... Daoud, 2009;Daoud and Daoud, 2009;Othman et al., 2004 for more reading). For example, Daoud (2009) and Daoud and Daoud (2009) proposed to use Universal Networking Language (UNL) and EnCo 2 ; a rule-based programming language specialized for the writing of EnConverters (i.e., parsers) to define disambiguation rules. They define several types of rules modeling morphological and syntactic contextual dependencies. ...
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.
In communication, textual data are a vital attribute. In all languages, ambiguous or polysemous words' meaning changes depending on the context in which they are used. The ability to determine the ambiguous word's correct meaning is a Know‐distill challenging task in natural language processing (NLP). Word sense disambiguation (WSD) is an NLP process to analyze and determine the correct meaning of polysemous words in a text. WSD is a computational linguistics task that automatically identifies the polysemous word's set of senses. Based on the context some word comes into view, WSD recognizes and tags the word to its correct priori known meaning. Semitic languages like Arabic have even more significant challenges than other languages since Arabic lacks diacritics, standardization, and a massive shortage of available resources. Recently, many approaches and techniques have been suggested to solve word ambiguity dilemmas in many different ways and several languages. In this review paper, an extensive survey of research works is presented, seeking to solve Arabic word sense disambiguation with the existing AWSD datasets.
This article is categorized under: Algorithmic Development > Text Mining
Technologies > Machine Learning
