A New Distributional Semantic Model for Classical Arabic (original) (raw)
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An Empirical Study On The Holy Quran Based On A Large Classical Arabic Corpus
2014
Distributional semantics is one of the empirical approaches to natural language processing and acquisition, which is mainly concerned by modeling word meaning using words distribution statistics gathered from huge corpora. Many distributional semantic models are available in the literature, but none of them have been applied so far to the Quran nor to Classical Arabic in general. This paper reports the construction of a very large corpus of Classical Arabic that will be used as a base to study distributional lexical semantics of the Quran and Classical Arabic. It also reports the results of two empirical studies; the first is applying a number of probabilistic distributional semantic models to automatically identify lexical collocations in the Quran and the other is applying those same models on the Classical Arabic corpus in an attempt to test their ability of capturing lexical collocations and co occurrences for a number of the corpus words. Results show that the MI.log_freq association measure achieved the highest results in extracting significant co-occurrences and collocations from small and large Classical Arabic corpora, while mutual information association measure achieved the worst results.
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2014
Classical Arabic forms the basis of Arabic linguistic theory and it is well understood by the educated Arabic reader. It is different in many ways from Modern Standard Arabic which is more simplified in its lexical, syntactic, morphological, phraseological and semantic structure. King Saud University Corpus of Classical Arabic is a pioneering corpus of around 50 million words of Classical Arabic. It is initially constructed for the purpose of studying distributional lexical semantics of the Quran and Classical Arabic, however, it is designed in a general way making it also appropriate for other researches in Linguistics and Computational Linguistics. In this paper, we will briefly describe the structure of our corpus, and then we will demonstrate how it can be used to depict some aspect of Arabic language change between the classical and the modern periods.
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2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), 2016
In this contribution we illustrate the methodology and the results of an experiment we conducted by applying Distributional Semantics Models to the analysis of the Holy Quran. Our aim was to gather information on the potential differences in meanings that the same words might take on when used in Modern Standard Arabic w.r.t. their usage in the Quran. To do so we used the Penn Arabic Treebank as a contrastive corpus.
Jurnal Teknologi Informasi dan Terapan, 2020
The Qur'an is the Muslim holy book as the main source and guide, consisting of 114 surahs, 30 juz and has 6200 fewer verses in it. The search for relationships or arrangements of meaning between words in the Qur'an takes a long time to find and summarize. Obtained from the dictionary, encyclopedia, or thesaurus of the Al-Qur'an vocabulary, which contains each word entry has links with other words. This final project discusses the interrelations and semantic correspondence between words in the Qur'an, which supports to help find inter-related words in it, using linking with distributions that involve important parts in the word embedding. Measurement of the relevance of the word measurement with semantic similarity which is one of the lessons learned in Natural Language Processing (NLP). Extraordinary similarity measures the proximity of a word vector using cosine similarity. The process of converting words in the form of vectors using the fasttext which is the develo...
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Mining the Web for the Induction of a Dialectical Arabic Lexicon
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