Abstract: Techniques and constructs to facilitate spelling correction of email queries can leverage features of email data to obtain candidate corrections particular to the email data being queried. The constructs may enable accurate spelling correction of email queries across languages and domains based on, for example, one or more of a language model such as a bigram language model and/or a normalized token IDF based language model, a translation model such as an edit distance translation model and/or a fuzzy match translation model, content-based features, and/or contextual features. Content-based features can include features associated with the subject line of emails, content including identified phrases, contacts, and/or the number of candidate emails returned. Contextual features can include a time window of subject match and/or contact match, a frequency of emails received from a contact, and/or device characteristics.

Abstract: Techniques and constructs to facilitate spelling correction of email queries can leverage features of email data to obtain candidate corrections particular to the email data being queried. The constructs may enable accurate spelling correction of email queries across languages and domains based on, for example, one or more of a language model such as a bigram language model and/or a normalized token IDF based language model, a translation model such as an edit distance translation model and/or a fuzzy match translation model, content-based features, and/or contextual features. Content-based features can include features associated with the subject line of emails, content including identified phrases, contacts, and/or the number of candidate emails returned. Contextual features can include a time window of subject match and/or contact match, a frequency of emails received from a contact, and/or device characteristics.