Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)

نویسندگان

  • Giuliano Lancioni
  • Valeria Pettinari
  • Laura Garofalo
  • Marta Campanelli
  • Ivana Pepe
  • Simona Olivieri
  • Ilaria Cicola
چکیده

An extended, revised form of Tim Buckwalter’s Arabic lexical and morphological resource AraMorph, eXtended Revised AraMorph (henceforth XRAM), is presented which addresses a number of weaknesses and inconsistencies of the original model by allowing a wider coverage of real-world Classical and contemporary (both formal and informal) Arabic texts. Building upon previous research, XRAM enhancements include (i) flag-selectable usage markers, (ii) probabilistic mildly context-sensitive POS tagging, filtering, disambiguation and ranking of alternative morphological analyses, (iii) semi-automatic increment of lexical coverage through extraction of lexical and morphological information from existing lexical resources. Testing of XRAM through a front-end Python module showed a remarkable success level.

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عنوان ژورنال:
  • CoRR

دوره abs/1603.01833  شماره 

صفحات  -

تاریخ انتشار 2016