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 embed-dings. We conduct an extensive study over 37 , 000 manually annotated queries and report performance of 90 . 92 F 1 independent of the query length.
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Recognizing Salient Entities in Shopping Queries
Semantic Scholar · Business · 2016
Abstract
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 embed-dings. We conduct an extensive study over 37 , 000 manually annotated queries and report performance of 90 . 92 F 1 independent of the query length.