Classification engine for learning properties of words and multi-word expressions

• US 11,100,287 B2
• Filed: 10/30/2018
• Issued: 08/24/2021
• Est. Priority Date: 10/30/2018
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Rabinovich, Ella, Sznajder, Benjamin, Spector, Artem, Shnayderman, Ilya, Aharonov, Ranit, Konopnicki, David, Slonim, Noam
• Primary Examiner(s): Shah, Bharatkumar S
• Application Number:
  US16/174,819
• Publication Number:
  US 20200134020A1
• Time in Patent Office: 1,029 Days
• Field of Search: 704 9
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Method and apparatus for training and using a classifier for words. Embodiments include receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class. Embodiments include training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences. Embodiments include determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier. Embodiments include using the measure of correlation to perform an action selected from the following list: selecting content to provide to a user; determining an automatic chat response; or filtering a set of content.

Claims

1. A method comprising:

receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class;

training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences;

determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier; and

using the measure of correlation to perform an action selected from a group comprising;

selecting content to provide to a user;

determining an automatic chat response;

or

filtering a set of content.

2. The method of claim 1, wherein training the classifier comprises identifying a context of the first word in the first plurality of sentences and a context of the second word in the second plurality of sentences.

3. The method of claim 2, wherein identifying the context of the first word in the first plurality of sentences and the context of the second word in the second plurality of sentences comprises identifying words near the first word in the first plurality of sentences and identifying words near the second word in the second plurality of sentences.

4. The method of claim 1, wherein the positive training data comprises one or more of the following:

a word;

a phrase;

a prefix;

or a suffix.

5. The method of claim 1, wherein the classifier comprises one of:

a nearest neighbor algorithm;

or a Nave Bayes algorithm.

6. The method of claim 1, wherein selecting the content to provide to the user comprises identifying anecdotal content based on the measure of correlation.

7. The method of claim 1, wherein determining the automatic chat response comprises:

determining a difficulty of a problem based on the measure of correlation; and

choosing whether to escalate the problem to a live support agent based on the difficulty of the problem.

8. The method of claim 1, wherein the class comprises words defined as being abstract.

9. The method of claim 1, wherein training the classifier comprises filtering out non-alphabetic words and special characters from the first plurality of sentences and the second plurality of sentences.

10. The method of claim 1, wherein the classifier is associated with one or more particular psycholinguistic classes.

11. A system, comprising:

one or more processors; and

a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform a method, the method comprising;

receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class;

training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences;

determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier; and

using the measure of correlation to perform an action selected from a group comprising;

selecting content to provide to a user;

determining an automatic chat response;

or

filtering a set of content.

12. The system of claim 11, wherein training the classifier comprises identifying a context of the first word in the first plurality of sentences and a context of the second word in the second plurality of sentences.

13. The system of claim 12, wherein identifying the context of the first word in the first plurality of sentences and the context of the second word in the second plurality of sentences comprises identifying words near the first word in the first plurality of sentences and identifying words near the second word in the second plurality of sentences.

14. The system of claim 11, wherein the positive training data comprises one or more of the following:

a word;

a phrase;

a prefix;

or a suffix.

15. The system of claim 11, wherein the classifier comprises one of:

a nearest neighbor algorithm;

or a Nave Bayes algorithm.

16. The system of claim 11, wherein selecting the content to provide to the user comprises identifying anecdotal content based on the measure of correlation.

17. The system of claim 11, wherein determining the automatic chat response comprises:

determining a difficulty of a problem based on the measure of correlation; and

choosing whether to escalate the problem to a live support agent based on the difficulty of the problem.

18. The system of claim 11, wherein the class comprises words defined as being abstract.

19. The system of claim 11, wherein training the classifier comprises filtering out non-alphabetic words and special characters from the first plurality of sentences and the second plurality of sentences.

20. A non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform a method, the method comprising:

receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class;

training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences;

determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier; and

using the measure of correlation to perform an action selected from a group comprising;

selecting content to provide to a user;

determining an automatic chat response;

or

filtering a set of content.