Meta learning for question classification

• US 20070203863A1
• Filed: 04/24/2006
• Published: 08/30/2007
• Est. Priority Date: 02/01/2006
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Gupta, Rakesh, Swarup, Samarth
• Granted Patent: US 7,603,330 B2
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

A system and a method are disclosed for automatic question classification and answering. A multipart artificial neural network (ANN) comprising a main ANN and an auxiliary ANN classifies a received question according to one of a plurality of defined categories. Unlabeled data is received from a source, such as a plurality of human volunteers. The unlabeled data comprises additional questions that might be asked of an autonomous machine such as a humanoid robot, and is used to train the auxiliary ANN in an unsupervised mode. The unsupervised training can comprise multiple auxiliary tasks that generate labeled data from the unlabeled data, thereby learning an underlying structure. Once the auxiliary ANN has trained, the weights are frozen and transferred to the main ANN. The main ANN can then be trained using labeled questions. The original question to be answered is applied to the trained main ANN, which assigns one of the defined categories. The assigned category is used to map the original question to a database that most likely contains the appropriate answer. An object and/or a property within the original question can be identified and used to formulate a query, using, for example, system query language (SQL), to search for the answer within the chosen database. The invention makes efficient use of available information, and improves training time and error rate relative to use of single part ANNs.

Claims

1. A method for automatically classifying a first question, the method comprising:

receiving unlabeled data;

automatically labeling said unlabeled data to produce first labeled data using a first artificial neural network, said first artificial neural network comprising a first set of weights;

transferring said first set of weights to a second artificial neural network;

receiving second labeled data comprising a second question and a corresponding answer;

training said second artificial neural network using said second labeled data;

receiving the first question; and

associating a question category with the first question using said second artificial neural network.

2. The method of claim 1, further comprising the step of mapping the first question to a first database of a plurality of databases based on said question category.

3. The method of claim 1, wherein said automatically labeling comprises learning a predictive structure within said unlabeled data.

4. The method of claim 1, wherein said unlabeled data comprises a third question.

5. The method of claim 3, wherein said automatically labeling said unlabeled data comprises at least one of:

automatically generating a part-of-speech tag for said third question;

automatically predicting a wh-word from one or more remaining words in said third question; and

automatically predicting a syntacetic relation-tuple between two words in said third question.

6. The method of claim 1, wherein said question category is one from the group consisting of:

facts-dates, facts-people, facts-other, response to object-property, response to person-action, implicit response to object-property, implicit response to person action, locations, observation-event-time, observation-object-status, observation-person-action, steps, web current events, and web weather.

7. The method of claim 1, further comprising the step of mapping said question category to a system query language (SQL) query.

8. The method of claim 1, wherein said first and second artificial neural networks each comprise an input layer, a hidden layer and an output layer.

9. The method of claim 1, wherein said training said second artificial neural network comprises forward activity and backward error propagation.

10. The method of claim 1, wherein said unlabeled data is contributed by human subjects via distributed data collection.

11. The method of claim 1, further comprising the step of freezing said first set of weights prior to transferring said first set of weights to said second artificial neural network.

12. The method of claim 2, further comprising the step of identifying at least one of an object and a property within said first question.

13. The method of claim 11, further comprising the step of querying said first database according to said object and said property.

14. The method of claim 1, wherein said second artificial neural network comprises a set of output nodes, and said question category is associated with the first question according to the strongest output node of said set of output nodes.

15. An apparatus for automatically classifying a first question, the apparatus comprising:

an input module configured to;

receive unlabeled data,

receive first labeled data comprising a second question and a corresponding answer, and

receive the first question;

a processor module configured to;

automatically label said unlabeled data to produce second labeled data using a first artificial neural network, said first artificial neural network comprising a first set of weights,

transfer said first set of weights to a second artificial neural network,

train said second artificial neural network using said first labeled data, and

associate a question category with the first question using said second artificial neural network.

16. The apparatus of claim 15, wherein said first and second artificial neural networks comprise a multipart artificial neural network.

17. An apparatus for automatically classifying a first question, the apparatus comprising:

means for receiving unlabeled data;

means for automatically labeling said unlabeled data to produce first labeled data using a first artificial neural network, said first artificial neural network comprising a first set of weights;

means for transferring said first set of weights to a second artificial neural network;

means for receiving second labeled data comprising a second question and a corresponding answer;

means for training said second artificial neural network using said second labeled data;

means for receiving the first question; and

means for associating a question category with the first question using said second artificial neural network.

18. The apparatus of claim 17, wherein said first and second artificial neural networks comprise a multipart artificial neural network.

19. A computer program product, comprising a computer-readable medium having computer program instructions embodied thereon to cause a computer processor to implement a method for automatically classifying a first question, the method comprising:

automatically labeling said unlabeled data to produce first labeled data using a first artificial neural network, said first artificial neural network comprising a first set of weights;

transferring said first set of weights to a second artificial neural network;

receiving second labeled data comprising a second question and a corresponding answer;

training said second artificial neural network using said second labeled data;

receiving the first question; and

associating a question category with the first question using said second artificial neural network.