E-commerce Query Tagging System Using Unsupervised Training Methods: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales. A key component of Amazon Search is the query understanding pipeline, which extracts appropriate semantic information used to precisely display products for billions of queries everyday. In this talk, we will go through the primary building blocks of query understanding pipeline.
Amazon Search enables users to search against structured products, hence it is necessary to extract information from queries in a format that is consistent with the structured information about the products. Query tagging is the task of semantically annotating query terms to pre-defined labels (such as brand, product-type and color). We propose a scalable system to train large-scale machine learning algorithms to solve this problem. Our system improved the precision over baseline, which is a dictionary lookup based tagger, by 10% and approximately doubled the recall.