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In e-commerce, a user tends to search for the desired product by issuing aquery to the search engine and examining the retrieved results. If the searchengine was successful in correctly understanding the user's query, it willreturn results that correspond to the products whose attributes match the termsin the query that are representative of the query's product intent. However,the search engine may fail to retrieve results that satisfy the query's productintent and thus degrading user experience due to different issues in queryprocessing: (i) when multiple terms are present in a query it may fail todetermine the relevant terms that are representative of the query's productintent, and (ii) it may suffer from vocabulary gap between the terms in thequery and the product's description, i.e., terms used in the query aresemantically similar but different from the terms in the product description.Hence, identifying the terms that describe the query's product intent andpredicting additional terms that describe the query's product intent betterthan the existing query terms to the search engine is an essential task ine-commerce search. In this paper, we leverage the historical queryreformulation logs of a major e-commerce retailer to develop distant-supervisedapproaches to solve both these problems. Our approaches exploit the fact thatthe significance of a term is dependent upon the context (other terms in theneighborhood) in which it is used in order to learn the importance of the termtowards the query's product intent. We show that identifying and emphasizingthe terms that define the query's product intent leads to a 3% improvement inranking. Moreover, for the tasks of identifying the important terms in a queryand for predicting the additional terms that represent product intent,experiments illustrate that our approaches outperform the non-contextualbaselines.

Intent term selection and refinement in e-commerce queries