This thesis explores the use of Natural Language Processing (NLP) on the Akkadian language documented from 2400 BCE to 100 CE. The methods and tools proposed in this thesis aim to fill the gaps left in previous research in Computational Assyriology, contributing to the transformation of transliterated cuneiform tablets into richly annotated text corpora, as well as to the quantitative lexicographic analysis of cuneiform texts.\n\nThree contributions of this thesis address the task of transforming Akkadian from its basic Latinized representation, transliteration, into linguistically annotated text corpora. These include (I) neural network-based automatic phonological transcription of transliterated cuneiform text, which is essential for normalizing the diverse spelling variations encountered in the Akkadian writing system; (II) finite-state-based automatic morphological analysis of Akkadian that allows deconstructing word forms into morphological labels, lemmata and part-of-speech tags to improve the useability of Akkadian corpora for quantitative analysis; and (III) creation of a morphological gold standard, and a standardized Universal Dependencies approved morphological label set for Akkadian morphology as the byproduct of an Akkadian treebank. \n\nThree contributions address the previously unexplored quantitative analysis of Akkadian lexical semantics using word association measures and word embeddings in order to better understand the language in its own terms. One of these contributions is (IV) an algorithmic method for reducing the distortion caused by fully or partially duplicated sequences in Akkadian texts. This algorithm solves over-representation issues encountered in pointwise mutual information (PMI)-based collocation analysis, and according to preliminary results, also in PMI-based word embeddings. Two contributions (V and VI) are quantitative case studies that demonstrate the use of PMI and word embeddings in Akkadian lexicography, and compare the results with previous qualitative philological research.\n\nThe last contribution (VII) is a hybrid approach, where PMI is applied to social network analysis of the Neo-Assyrian pantheon in order to reinforce the statistical relevance between the actors. These "semantic" social networks are used to study the position of the Assyrian main god, Aššur, within the pantheon.\n\nIn addition to the contributions, this thesis presents the first survey of Computational Assyriology, which covers six decades of research on automatic artifact reconstruction, optical character recognition, linguistic annotation, and quantitative analysis of cuneiform texts.