Recognizing Salient Entities in Shopping Queries

Over the past decade, e-Commerce has rapidly grown enabling customers to purchase products with the click of a button. But to be able to do so, one has to understand the semantics of a user query and identify that in digital lifestyle tv, digital lifestyle is a brand and tv is a product. In this paper, we develop a series of structured prediction algorithms for semantic tagging of shopping queries with the product, brand, model and product family types. We model wide variety of features and show an alternative way to capture knowledge base information using embeddings. We conduct an extensive study over 37, 000 manually annotated queries and report performance of 90.92 F1 independent of the query length.