Amazon Technologies, Inc.

Bernhard Wolkerstorfer

Lei Li

Narendra S Parihar

Sherif M Yacoub

Dongmei Jia

Nicholas Alan Tostenrude

Stephen Kang

Gerald J Strode

A method and system for classifying document data is described. The method may include classifying a first portion of an electronic document as substantive content or noise, classifying a second portion of the electronic document as substantive content or noise, determining a first feature of the first portion of the electronic document indicative of substantive content using a machine learning algorithm, and determining a second feature of the second portion of the electronic document indicative of noise using the machine learning algorithm.

Document data classification using a noise-to-content ratio

A method and system for classifying document data is described. An exemplary method includes identifying a markup language document having a plurality of portions, determining a set of substantive content metrics and a set of noise metrics for each of the plurality of portions, calculating a noise-to-content ratio for each of the plurality of portions based on a corresponding set of substantive content metrics and a corresponding set of noise metrics, and removing noise from the markup language document using the noise-to-content ratio.

Techniques are described for identifying potentially interesting portions of a content item to be provided as a preview of the content item for prospective purchasers, based on previously collected data associated with the content item. Portions of a content item may be identified as potentially interesting based on a number of annotations (e.g., highlights, bookmarks, notes, and shares) previously made by viewers of a digital version of the content item. Potentially interesting portions may also include portions which prior viewers spent more time viewing, portions related to identified interests of the potential buyer, portions that are identified as separable for particular categories of content, and/or portions that have been previously identified as associated with elements of the content such as character, plot, and/or keywords.

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Seattle, WA, United States of America

Bernhard Wolkerstorfer