Toward Automatically Assembling Hittite-Language Cuneiform Tablet Fragments into Larger Texts

This paper presents the problem within Hittite and Ancient Near Eastern studies of fragmented and damaged cuneiform texts, and proposes to use well-known text classification metrics, in combination with some facts about the structure of Hittite-language cuneiform texts, to help classify a number of fragments of clay cuneiform-script tablets into more complete texts. In particular, I propose using Sumerian and Akkadian ideogrammatic signs within Hittite texts to improve the performance of Naive Bayes and Maximum Entropy classifiers. The performance in some cases is improved, and in some cases very much not, suggesting that the variable frequency of occurrence of these ideograms in individual fragments makes considerable difference in the ideal choice for a classification method. Further, complexities of the writing system and the digital availability of Hittite texts complicate the problem.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Stephen Tyndall
• Keywords: ACL 2012, Computational Linguistics, Cuneiform Texts, Hittite Texts, Naive Bayes