  Historic dress artifacts are a valuable source for human studies. In
particular, they can provide important insights into the social aspects of
their corresponding era. These insights are commonly drawn from garment
pictures as well as the accompanying descriptions and are usually stored in a
standardized and controlled vocabulary that accurately describes garments and
costume items, called the Costume Core Vocabulary. Building an accurate Costume
Core from garment descriptions can be challenging because the historic garment
items are often donated, and the accompanying descriptions can be based on
untrained individuals and use a language common to the period of the items. In
this paper, we present an approach to use Natural Language Processing (NLP) to
map the free-form text descriptions of the historic items to that of the
controlled vocabulary provided by the Costume Core. Despite the limited
dataset, we were able to train an NLP model based on the Universal Sentence
Encoder to perform this mapping with more than 90% test accuracy for a subset
of the Costume Core vocabulary. We describe our methodology, design choices,
and development of our approach, and show the feasibility of predicting the
Costume Core for unseen descriptions. With more garment descriptions still
being curated to be used for training, we expect to have higher accuracy for
better generalizability.
