Textual Entailment for Modern Standard Arabic (Informatica, 2021)
This paper summarizes the Doctoral Thesis that examines various techniques to recognizing Arabic textual entailment, deciding whether one fragment of text entails another, where there is an exceptional level of structural and lexical ambiguities. As far as we know, the current work is the first study to apply this task for Arabic. For this purpose, we firstly describe a semi-automatic method for constructing a first Arabic textual entailment dataset. Then, we have investigated various system combination techniques for improving tagging and parsing depending on having accurate linguistic analyses. Finally, we have improved the standard tree edit distance (TED) algorithm. This extended version of TED, ETED, calculates the distance between two trees by applying operations on subtrees and single nodes. The current work also uses the artificial bee colony (ABC) algorithm to automatically guess the edit operations cost for both subtrees and single nodes and to decide thresholds. The current findings were encouraging for Arabic and English RTE-2 test sets.