The Design and the Construction of the Traditional Arabic Lexicons Corpus (The TAL-Corpus)


  •  Majdi Sawalha    

Abstract

Arabic lexicography is a well-established and deep-rooted art of Arabic literature. Computational lexicography, invests computational and storage powers of modern computers, to accelerate long-term efforts in lexicographic projects. A collection of 23 machine-readable dictionaries, which are freely available on the web, were used to build the Corpus of Traditional Arabic lexicons (the TAL-Corpus). The purpose for constructing the TAL-Corpus is to collect and organize well-established and long traditions of traditional Arabic lexicons which can also be used to create new corpus-based Arabic dictionaries. The compilation of the TAL-Corpus followed standard design and development criteria that informed major decisions for corpus creation. The corpus building process involved extracting information from disparate formats and merging traditional Arabic lexicons. As a result, the TAL-Corpus contains more than 14 million words and over 2 million word types (different words).  The TAL-Copus was applied to create useful morphological database. This database was automatically constructed using a new algorithm which is informed by Arabic linguistics theory. The newly developed algorithm processed the text of the TAL-Corpus and as result it extracted 2 781 796 entries. These entries were stored in the morphological database where each represents a word-root pair (i.e. an Arabic word and its root). A comparative evaluation of the TAL-Corpus and other three Arabic corpora showed that the lexical diversity of its vocabulary scored higher. Moreover, its coverage was computed by comparing words and lemmas against their equivalents of other corpora where it scored about 67% when comparing words and 82% when comparing lemmas.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-1844
  • Issn(Onlne): 1913-1852
  • Started: 2007
  • Frequency: monthly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 6.49

h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

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