Word Embeddings Pointing the Way for Late Antiquity

Abstract

Continuous space representations of words are currently at the core of many state-of-the-art approaches to problems in natural language processing. In spite of several advantages of using such methods, they have seen little usage within digital humanities. In this paper, we show a case study of how such models can be used to find interesting relationships within the field of late antiquity. We use a word2vec model trained on over one billion words of Latin to investigate the relationships between persons and concepts of interest from works of the 6th-century scholar Cassiodorus. The results show that the method has high potential to aid the humanities scholar, but that caution must be taken as the analysis requires the assessment by the traditional historian. c 2015 Association for Computational Linguistics and The Asian Federation of Natural Language Processing.