The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Oct. 25, 2016
Filed:
Apr. 01, 2014
Microsoft Corporation, Redmond, WA (US);
Xiaodong He, Issaquah, WA (US);
Jianfeng Gao, Woodinville, WA (US);
Li Deng, Redmond, WA (US);
Qiang Lou, Bellevue, WA (US);
Yunhong Zhou, Bellevue, WA (US);
Guowei Liu, Sammamish, WA (US);
Gregory T. Buehrer, Issaquah, WA (US);
Jianchang Mao, Bellevue, WA (US);
Yelong Shen, Redmond, WA (US);
Ruofei Zhang, Mountain View, CA (US);
Microsoft Corporation, Redmond, WA (US);
Abstract
Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.