Entity Set Expansion for Detecting Fashion Trends

The Fashion industry revolves around Fashion trends. Designers and merchandisers plan products based on predicted trends, and online catalogs sell new trends to customers. An important part of tracking trends is tracking the ever-evolving fashion terminology. We tackle this problem by expanding a given fashion entity set using automatic synonym discovery in an external fashion related corpus, thus adding new terms to the fashion vocabulary. This paper presents supervised methods for synonym discovery that modify two unsupervised methods based on linear and non-linear contexts, respectively. We demonstrate that the task of synonym discovery benefits from using non-linear context as opposed to linear contexts for learning word embeddings.