Spoken language understanding that incorporates prior knowledge into boosting

• US 7,328,146 B1
• Filed: 07/11/2006
• Issued: 02/05/2008
• Est. Priority Date: 05/31/2002
• Status: Expired due to Fees

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: Alshawi, Hiyan, DiFabrizzio, Giuseppe, Gupta, Narendra K., Rahim, Mazin G., Schapire, Robert E., Singer, Yoram
• Primary Examiner(s): Smits, Talivaldis Ivars
• Assistant Examiner(s): Ng, Eunice
• Application Number:
  US11/484,120
• Time in Patent Office: 574 Days
• Field of Search: None
• US Class Current: 704/1
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30

Abstract

A system for understanding entries, such as speech, develops a classifier by employing prior knowledge with which a given corpus of training entries is enlarged threefold. A rule is created for each of the labels employed in the classifier, and the created rules are applied to the given corpus to create a corpus of attachments by appending a weight of ηp(x), or 1−ηp(x), to labels of entries that meet, or fail to meet, respectively, conditions of the labels'"'"' rules, and to also create a corpus of non-attachments by appending a weight of 1−ηp(x), or ηp(x), to labels of entries that meet, or fail to meet conditions of the labels'"'"' rules.

Claims

1. A method of understanding presented data in the context of a particular application, comprising the steps of:

normalizing said data to reduce variations in said presented data, to develop normalized data;

assigning portions of said normalized data to be instances of objects from a set of preselected objects when said portions of said normalized data meet predetermined conditions, thereby forming entity-extracted data; and

classifying said entity-extracted data by determining whether any of a predetermined set of labels should be attached to said entity-extracted data where said classifying is carried out with a classifier represented by

$f<br><br>\left(x,l\right)=\underset{t=0}{\overset{T}{\sum <br><br>}}<br><br><br><br>{\underset{\_}{\mathrm{\alpha <br><br>}}}_{\underset{\_}{t}}<br><br>h_t<br><br>\left(x,l\right),$

where h_i(x,l) is a classifier that classifies an entity-extracted data x with respect to label l, α

_t is a preselected constant, and

each classifier h<sub>i−

1</sub>(x,l) is developed, in part, from classifier h_i(x,l).

2. The method of claim 1 where said understanding of presented data is relative to a particular application, and said classifier is developed from a constructed corpus of training entries that is at Mm in the size, where m is size of a provided corpus of training entries, M is a positive integer, and said constructed corpus of training entries is derived from said provided corpus of training sequences.

3. The method of claim 2 where said classifier is developed with a rule established with prior knowledge of said particular application.

4. The method of claim 2 where M is either 3 or 2.