Abstract: A method including extracting queries from historical search query data. The method also can include spellchecking each of the queries (i) using a first spellcheck model and (ii) using a second spellcheck model. A latency of the first spellcheck model is lower than a latency of the second spellcheck model, and an overall accuracy of the second spellcheck model is higher than an overall accuracy of the first spellcheck model. The method additionally can include determining first queries of the queries in which, for each of the first queries, a first respective spellcheck output from the first spellcheck model is different from a second respective spellcheck output from the second spellcheck model. The method further can include adding at least a portion of the first queries to a spellcheck cache for runtime spellchecking. Other embodiments are described.

Abstract: A data generation system can include a computing device that is configured to receive a request to generate a training dataset for an attribute and identify a set of item identifiers from an item database based on an engagement indication. The computing device is further configured to, for each item identifier of the set of item identifiers, obtain a query list including queries resulting in an engagement between the corresponding item identifier and a user and, in response to a portion of queries of the query list including the attribute being above a threshold, assign the corresponding item identifier to the training dataset for the attribute. The computing device is also configured to store the training dataset for the attribute in a training dataset database.