Ancient manuscripts are frequently damaged, containing gaps in the text known as lacunae. In this paper, we present a bidirectional RNN model for character prediction of Coptic characters in manuscript lacunae. Our best model performs with 72% accuracy on single character reconstruction, but falls to 37% when reconstructing lacunae of various lengths. While not suitable for definitive manuscript reconstruction, we argue that our RNN model can help scholars rank the likelihood of textual reconstructions. As evidence, we use our RNN model to rank reconstructions in two early Coptic manuscripts. Our investigation shows that neural models can augment traditional methods of textual restoration, providing scholars with an additional tool to assess lacunae in Coptic manuscripts.

Have you ever been embarrassed by the first iteration of one of your machine learning projects, where you didn't include obvious and important features? In the practical hustle and bustle of trying to build models, we can often forget about the observation step in the scientific method and jump straight to hypothesis testing. Data scientists and their models can benefit greatly from qualitative methods. Without doing qualitative research, data scientists risk making assumptions about how users behave. In this post, we'll explore how qualitative methods can help all data scientists build better models, using a case study of Indeed's new lead routing machine learning model which ultimately generated several million dollars in revenue.

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