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.

Date of Patent:
Apr. 22, 2014

Filed:

Aug. 24, 2010
Applicants:

Ciprian Chelba, Seattle, WA (US);

Milind Mahajan, Redmond, WA (US);

Inventors:

Ciprian Chelba, Seattle, WA (US);

Milind Mahajan, Redmond, WA (US);

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/27 (2006.01); G10L 15/00 (2013.01); G10L 15/18 (2013.01); G10L 15/04 (2013.01); G10L 15/05 (2013.01); G06F 17/20 (2006.01); G06F 17/28 (2006.01); G10L 15/22 (2006.01);
U.S. Cl.
CPC ...
G06F 17/2785 (2013.01); G10L 15/18 (2013.01); G10L 15/00 (2013.01); G10L 15/04 (2013.01); G10L 15/05 (2013.01); G10L 15/22 (2013.01); G06F 17/20 (2013.01); G06F 17/27 (2013.01); G06F 17/2705 (2013.01); G06F 17/271 (2013.01); G10L 15/1822 (2013.01); G06F 17/28 (2013.01); G06F 17/2881 (2013.01);
Abstract

One feature of the present invention uses the parsing capabilities of a structured language model in the information extraction process. During training, the structured language model is first initialized with syntactically annotated training data. The model is then trained by generating parses on semantically annotated training data enforcing annotated constituent boundaries. The syntactic labels in the parse trees generated by the parser are then replaced with joint syntactic and semantic labels. The model is then trained by generating parses on the semantically annotated training data enforcing the semantic tags or labels found in the training data. The trained model can then be used to extract information from test data using the parses generated by the model.


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