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The increasing availability of digital collections of historical andcontemporary literature presents a wealth of possibilities for new research inthe humanities. The scale and diversity of such collections however, presentsparticular challenges in identifying and extracting relevant content. Thispaper presents Curatr, an online platform for the exploration and curation ofliterature with machine learning-supported semantic search, designed within thecontext of digital humanities scholarship. The platform provides a text miningworkflow that combines neural word embeddings with expert domain knowledge toenable the generation of thematic lexicons, allowing researches to curaterelevant sub-corpora from a large corpus of 18th and 19th century digitisedtexts.

Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts