Amazon Technologies, Inc.

Divya Mahalingam

David R Azari

Tanvi Milind Bhadbhade

Mitsuo Takaki

David Gordon Leatham

William Tunstall-Pedoe

Jennifer Amy Sadler

Nadia C Payet

Oliver Nam Wong

Techniques are described for determining whether a submitted work, such as a manuscript submitted for publication as an electronic book, is consistent with other published works associated with a fictional world, universe, or other framework. A set of constraints may be determined based on a semantic analysis of the published works of a framework, the constraint(s) describing attributes of noun objects or relationships between noun objects in the published work(s). Statements may be determined by applying the semantic analysis to the submitted work. Inconsistencies between the statements and the constraints may be identified and communicated to reviewers, for use in determining whether to publish the work. Comparison of the statements to the constraints may be made through reference to other statements included in a knowledge base of information regarding the framework.

Natural language based content analysis

A system and method for determining whether a textual work submitted for publishing is machine generated or non-machine generated by identifying and quantifying various aspects of the textual work and comparing those aspects to known works. For example, the system and method may identify aspects of a textual work, including, a relationship between the sentences within the textual work, a writing style of the author of the textual work, a grammatical structure of the sentences within the textual work, a quality of the textual work, and other aspects of the textual work. Upon determining that the textual work is machine generated the textual work may be rejected for publishing.

Machined book detection

Techniques are described for analyzing a submitted manuscript through the use of an entity/action relationship matrix determined for the manuscript. The entity/action relationship matrix may describe one or more entities, such as characters, locations, objects and so forth, which are identified in the manuscript using a natural language analysis such as a part-of-speech analysis. The entity/action relationship matrix may also describe actions or events associated with each of the entities, as determined using the natural language analysis. The entity/action relationship matrix may be employed to determine whether the manuscript complies with rules constraining the use of entities in the manuscript.

Event detection based on entity analysis

One or more computing systems can implement a classifier to classify content publishers as being likely to provide appropriate content or as being likely to provide inappropriate content. The classifier can gather information from previously classified publishers. The information from the previously classified publishers can used to train the classifier. Based on the training, the classifier can learn about traits, characteristics, and/or behavioral patterns, etc., associated with publishers that have been previously classified as being good as well as publishers previously classified as being bad. The classifier can then process information about an unclassified publisher to determine a classification for the unclassified publisher, as being good (and likely to provide appropriate content) or bad (and likely to provide inappropriate content).

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Divya Mahalingam