Abstract
Because of the less-resourced nature of historical languages, non-standard solutions are often required for natural language processing tasks. This article introduces one such solution for historical-language lemmatization, that is the Ensemble lemmatizer for the Classical Language Toolkit, an open-source Python package that supports NLP research for historical languages. Ensemble lemmatization is the most recent development at CLTK in the repurposing and refactoring of an existing method designed for one task, specifically the backoff method as used for part-of-speech tagging, for use in a different task, namely lemmatization. This article argues for the benefits of ensemble lemmatization, specifically, flexible tool construction and the use of all available information to reach tagging decisions, and presents two use cases.