Automated message attachment labeling using feature selection in message content

摘要

Embodiments are directed towards an automated machine learning framework to extract keywords within a message that are relevant to an attachment to the message. The machine learning model finds a set of relevant sentences within the message determined to be relevant to the one or more attachments based on identification of one or more sentence level features within a given sentence. The sentence level features include, for example, anchor features, noisy sentence features, short message features, threading features, anaphora detections, and lexicon features. From the set of relevant sentences, useful keywords may be extracted using a sequence of heuristics to convert the sentence set into the set of useful keywords. The set of useful keywords may then be associated to at least one attachment such that the keywords may subsequently be used to perform various indexing, searching, sorting, and to provide further context to the attachment.