Method and apparatus for adapting a search classifier based on user queries

• US 7,266,559 B2
• Filed: 12/05/2002
• Issued: 09/04/2007
• Est. Priority Date: 12/05/2002
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Seiber, Scott E., Cook, Daniel B., Goldberg, Matthew A., Oftedal, Chad S.
• Primary Examiner(s): Truong; Cam Y
• Assistant Examiner(s): Ly; Anh
• Application Number:
  US10/310,408
• Publication Number:
  US 20040111419A1
• Time in Patent Office: 1,734 Days
• Field of Search: 707 1- 6, 707/10, 707100-1041, 706/25, 706/26, 706/45, 706/12, 382/159, 382/224, 704/9
• US Class Current: 1/1
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A method and computer-readable medium are provided for constructing a classifier for classifying search queries. The classifier is constructed by receiving a query from a user and applying the query to a classifier to identify the task. An unsupervised mapping between the query and the task is then identified and is used to train a new classifier. Under one embodiment, the unsupervised mapping is identified based on a user'"'"'s selection of the task.

Claims

1. A method of constructing a classifier for classifying user inputs, the method comprising:

receiving a plurality of user inputs, wherein a user input comprises a user query;

applying each user query to a first classifier that resides in a computing device to identify a respective task;

for each user query, displaying at least one task to a user;

logging a task selected by the user;

creating separate unsupervised mappings between each user query and each respective task identified by the first classifier, wherein creating an unsupervised mapping comprises determining a confidence level for the unsupervised mapping;

selecting fewer than all of the unsupervised mappings to train a new classifier wherein selecting an unsupervised mapping comprises determining that the confidence level for the unsupervised mapping is within a range of desired confidence levels and selecting the unsupervised mapping based on that determination; and

training the new classifier based on the selected unsupervised mapping, the new classifier for classifying user queries into tasks.

2. The method of claim 1 further comprising testing the performance of the new classifier and replacing the first classifier with the new classifier if the performance of the new classifier is better than the performance of the first classifier.

3. The method of claim 1 further comprising using an unsupervised mapping to train a second new classifier.

4. The method of claim 3 further comprising testing the new classifier and the second new classifier to determine which is a better performing new classifier.

5. The method of claim 4 further comprising replacing the first classifier with the better performing new classifier.

6. The method of claim 5 wherein replacing the first classifier comprises only replacing the first classifier if the better performing new classifier performs better than the first classifier.

7. The method of claim 1 wherein selecting fewer than all of the unsupervised mappings comprises selecting the mappings based on when they were produced.