The extensive surviving corpus of the ancient scholar Plutarch of Chaeronea (ca. 45-120 CE) also contains several texts which, according to current scholarly opinion, did not originate with him and are therefore attributed to an anonymous author Pseudo-Plutarch. These include, in particular, the work Placita Philosophorum (Quotations and Opinions of the Ancient Philosophers), which is extremely important for the history of ancient philosophy. Little is known about the identity of that anonymous author and its relation to other authors from the same period. This paper presents a BERT language model for Ancient Greek. The model discovers previously unknown statistical properties relevant to these literary, philosophical, and historical problems and can shed new light on this authorship question. In particular, the Placita Philosophorum, together with one of the other Pseudo-Plutarch texts, shows similarities with the texts written by authors from an Alexandrian context (2nd/3rd century CE).

Plutarch's Lives of the Noble Greeks and Romans, also called Parallel Lives or just Plutarch's Lives, is a series of biographies of famous Ancient Greeks and Romans, from Theseus and Lycurgus to Marcus Antonius. In this article / tutorial -- following the recently published Part 1-- I will continue exploring this book using some of the natural language processing techniques. To help with an easy replication, I adapted the code to Google Colab, and highlighted what is unique to the platform -- otherwise the entire code can be run locally on Python 3.6 . The code is sequentially presented throughout the article and the link to the Github files is embedded at the end as I may skip some minor details or supplementary code. The text used in this analysis has been made available by Project Gutenberg.

In the model summary we'll see that the number of parameters for the embedding layer is 2,024,200, which is 20,242 words times the embedding dimension of 100. The previously mentioned TensorFlow tutorial is using a reviews dataset with each of the reviews being labeled 1 or 0 depending on the positive or negative sentiment. We do not have the labeling luxury but still want to test drive this model, so will simply create an array of 0s and attach to each of the sentences; the model requires such a structure. This will not be the first or the last time that machine intelligence gets assaulted with an unsolvable task yet still obliges us with a solution.