Google Inc.

Buzzmetrics, Ltd.

Buzzmetrics, Ltd an Israel Corporation

Kamal P Nigam

Yunkai Zhou

Mark Matthew Palatucci

Niels Provos

Clayton Woodward Bavor, Jr

Eric L Davis

Panayiotis Mavrommatis

Rachel Nakauchi

Christopher K Monson

Matthew F Hurst

Kawaljit Gandhi

Nitin Mangtani

Robert G Stockton

Yuk Wah Wong

Dominic Widdows

Thomas David Lokovic

Carl J Evankovich

Abdulrahman Salem

Clayton W Bavor Jr

Jr Clayton W Bavor

Intrusion features of a landing page associated with sponsored content are identified. A feature score for the landing page based on the identified intrusion features is generated, and if the feature score for the landing page exceeds a feature threshold, the landing page is classified as a candidate landing page. A sponsor account associated with the candidate landing page can be suspended, or sponsored content associated with the candidate landing page can be suspended.

Detection of malware features in a content item

Intrusive software management

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for attribute-value extraction from structured documents. In one aspect, a method includes obtaining an initial attribute whitelist, extracting candidate attributes from a first collection of documents, and grouping the candidate attributes. The method further includes calculating a score for each unique attribute in the candidate attributes, generating an expanded attribute whitelist including the initial attributes and each unique attribute having a score that satisfies a threshold, and using the expanded attribute whitelist to identify valid attribute-value pairs. In another aspect, a method includes extracting candidate attribute-value pairs from a collection of documents and identifying one or more features for each candidate attribute-value pair. The method further includes filtering out non valid attribute-value pairs.

Attribute-value extraction from structured documents

System, methods and computer program products for identifying a featured product based on product information submitted by retailers and manufacturers are described. In some implementations, a retailer and a manufacturer can supply product information to an ad serving system. Based on the product information supplied by the retailer and the manufacturer, a product matching the product can be identified. The matching product can then be presented on a content of the retailer that links to a product page of the matching product.

System and method for presenting content on a retailer's content page using manufacturers' product information

The present application presents methods for performing topical sentiment analysis on electronically stored communications employing fusion of polarity and topicality. The present application also provides methods for utilizing shallow NLP techniques to determine the polarity of an expression. The present application also provides a method for tuning a domain-specific polarity lexicon for use in the polarity determination. The present application also provides methods for computing a numeric metric of the aggregate opinion about some topic expressed in a set of expressions.

Topical sentiments in electronically stored communications

Detecting an intrusive landing page

Landing pages associated with advertisements are partitioned into training landing pages and testing landing pages. Iterative training and testing of a classification mode on intrusion features of the partitioned landing pages is conducted until the occurrence of a cessation event. Feature weights are derived from the iterative training and testing, and are associated with the intrusion features. The associated feature weights and intrusion features can be used to classify other landing pages.

Intrusive feature classification model

A computer assisted/implemented method for developing a classifier for classifying communications includes roughly four stages, where these stages are designed to be iterative: (1) a stage defining where and how to harvest messages (i.e., from Internet message boards, ews groups and the like), which also defines an expected domain of application for the lassifier; (2) a guided question/answering stage for the computerized tool to elicit the user's criteria for determining whether a message is relevant or irrelevant; (3) a labeling stage where the user examines carefully-selected messages and provides feedback about whether or not it is relevant and sometimes also what elements of the criteria were used to make the decision; and (4) a performance evaluation stage where parameters of the classifier training are optimized, the best classifier is produced, and known performance bounds are calculated. In the guided question/answering stage, the criteria are parameterized in such a way that (a) they can be operationalized into the text classifier through key words and phrases, and (b) a human-readable criteria can be produced, which can be reviewed and edited. The labeling phase is oriented toward an extended Active Learning framework. That is, the exemplary embodiment decides which example messages to show the user based upon what category of messages the system thinks would be most useful to the Active Learning process.

Method for developing a classifier for classifying communications

Growing community of inventors

Pittsburgh, PA, United States of America

Kamal P Nigam