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

Autor: Giuliano Lancioni, Laura Garofalo, Raoul Villano, Francesca Romana Romani, Marta Campanelli, Ilaria Cicola, Ivana Pepe, Valeria Pettinari, Simona Olivieri
Přispěvatelé: Neamat El Gayar, Ching Y. Suen, Lancioni, Giuliano, Garofalo, Laura, Villano, Raoul, Romani, Francesca Romana, Campanelli, Marta, Cicola, Ilaria, Pepe, Ivana, Pettinari, Valeria, Olivieri, Simona
Rok vydání: 2018
Předmět:
DOI: 10.1142/9789813229396_0007
Popis: 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.
Databáze: OpenAIRE