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Historic dress artifacts are a valuable source for human studies. Inparticular, they can provide important insights into the social aspects oftheir corresponding era. These insights are commonly drawn from garmentpictures as well as the accompanying descriptions and are usually stored in astandardized and controlled vocabulary that accurately describes garments andcostume items, called the Costume Core Vocabulary. Building an accurate CostumeCore from garment descriptions can be challenging because the historic garmentitems are often donated, and the accompanying descriptions can be based onuntrained individuals and use a language common to the period of the items. Inthis paper, we present an approach to use Natural Language Processing (NLP) tomap the free-form text descriptions of the historic items to that of thecontrolled vocabulary provided by the Costume Core. Despite the limiteddataset, we were able to train an NLP model based on the Universal SentenceEncoder to perform this mapping with more than 90% test accuracy for a subsetof the Costume Core vocabulary. We describe our methodology, design choices,and development of our approach, and show the feasibility of predicting theCostume Core for unseen descriptions. With more garment descriptions stillbeing curated to be used for training, we expect to have higher accuracy forbetter generalizability.

Using Natural Language Processing to Predict Costume Core Vocabulary of Historical Artifacts