IP Library › Granted Patent US 11,074,411
Granted Patent B2
US 11,074,411 · App. 16/658,988 · Granted Jul 27, 2021

Disambiguation of concept classifications using language-specific clues

Inventors: Changying Sun (Ottawa, CA); Lin Luo (Ottawa, CA); Graham Wills (Naperville, IL)
Assignee: International Business Machines Corporation
G06F40/284G06F16/328G06F16/3329G06F40/263
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,074,411
App. No.
16/658,988
Granted
Jul 27, 2021
Kind
B2
Abstract

A computer-implemented method, system, and computer program product for classifying a concept of a data item. A data item label for the data item is received. The data item label is analyzed using a natural language processing tool to generate additional lexical information for the data item label. A search query is built using the additional lexical information for the data item label. The search query is used to search a clue concept index to produce a search result. The clue concept index comprises clue concept records that identify clues for concepts. The search result identifies clue concept records from the clue concept index that match the search query. A concept is selected from the clue concept records identified in the search result as the concept for the data item.

Claims (80)

1. A computer-implemented method of classifying a concept of a data item, comprising:

receiving a data item label for the data item;

analyzing the data item label using a natural language processing tool to generate additional lexical information for the data item label;

building a search query using the additional lexical information for the data item label;

using the search query to search a clue concept index to produce a search result, wherein the clue concept index comprises clue concept records that identify clues for concepts, and wherein the search result identifies clue concept records from the clue concept index that match the search query; and

selecting a concept from the clue concept records identified in the search result as the concept for the data item.

2. The computer-implemented method of claim 1 , wherein:

the additional lexical information for the data item label comprises tokens, wherein each token in the tokens comprises text from the data item label and a lemma of the text from the data item label; and

wherein building the search query comprises building the search query using the text and lemma from each token in the tokens for the data item label.

3. The computer-implemented method of claim 1 , wherein the search result comprises a relevance score for each clue concept record identified in the search result, and further comprising:

modifying the relevance score for each clue concept record identified in the search result based on which field in the clue concept record is matched to the search query in the search to produce a boosted relevance score for each clue concept record identified in the search result; and

using the boosted relevance score to select a concept from the clue concept records identified in the search result as the concept for the data item.

4. The computer-implemented method of claim 3 further comprising:

determining a weighted relevance score for each clue concept record identified in the search result by weighting the boosted relevance score for each clue concept record identified in the search result with a weight determined by a scoring algorithm; and

selecting concepts in the clue concept record with the highest weighted relevance score as concepts for the data item.

5. The computer-implemented method of claim 4 , wherein weights determined by the scoring algorithm are trained by a machine learning model.

6. The computer-implemented method of claim 1 , comprising:

identifying a language of the data item label;

determining whether the language of the data item label is English;

in response to a determination that the language of the data item label is not English, using the search query to search a language-specific clue concept index for the language of the data item label and an English clue concept index;

in response to a determination that the language of the data item label is English, using the search query to search only an English clue concept index.

7. The computer implemented method of claim 1 further comprising, generating the clue concept index by:

receiving concept labels for concepts;

automatically generating lexical clues from the concept labels;

analyzing the lexical clues using a natural language processing tool to generate additional lexical information for the concepts;

generating the clue concept records using the lexical clues and additional lexical information as clues for the concepts; and

incorporating the clue concept records in the clue concept index.

8. A system for classifying a concept of a data item, comprising a data processing system configured to:

receive a data item label for the data item;

analyze the data item label using a natural language processing tool to generate additional lexical information for the data item label;

build a search query using the additional lexical information for the data item label;

use the search query to search a clue concept index to produce a search result, wherein the clue concept index comprises clue concept records that identify clues for concepts, and wherein the search result identifies clue concept records from the clue concept index that match the search query; and

select a concept from the clue concept records identified in the search result as the concept for the data item.

9. The system of claim 8 , wherein:

the additional lexical information for the data item label comprises tokens, wherein each token in the tokens comprises text from the data item label and a lemma of the text from the data item label; and

wherein the data processing system is configured to build the search query using the text and lemma from each token in the tokens for the data item label.

10. The system of claim 8 , wherein the search result comprises a relevance score for each clue concept record identified in the search result, and wherein the data processing system is further configured to:

modify the relevance score for each clue concept record identified in the search result based on which field in the clue concept record is matched to the search query in the search to produce a boosted relevance score for each clue concept record identified in the search result; and

use the boosted relevance score to select the concept from the clue concept records identified in the search result as the concept for the data item.

11. The system of claim 10 , wherein the data processing system is further configured to:

determine a weighted relevance score for each clue concept record identified in the search result by weighting the boosted relevance score for each clue concept record identified in the search result with a weight determined by a scoring algorithm; and

select concepts in the clue concept record with the highest weighted relevance score as concepts for the data item.

12. The system of claim 11 , wherein weights determined by the scoring algorithm are trained by a machine learning model.

13. The system of claim 8 , wherein the data processing system is configured to:

identify a language of the data item label;

determine whether the language of the data item label is English;

in response to a determination that the language of the data item label is not English, use the search query to search a language-specific clue concept index for the language of the data item label and an English clue concept index;

in response to a determination that the language of the data item label is English, use the search query to search only an English clue concept index.

14. The system of claim 8 , wherein the data processing system is further configured to generate the clue concept index by:

receiving concept labels for concepts;

automatically generating lexical clues from the concept labels;

analyzing the lexical clues using a natural language processing tool to generate additional lexical information for the concepts;

generating the clue concept records using the lexical clues and additional lexical information as clues for the concepts; and

incorporating the clue concept records in the clue concept index.

15. A computer program product for classifying a concept of a data item, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:

receive a data item label for the data item;

analyze the data item label using a natural language processing tool to generate additional lexical information for the data item label;

build a search query using the additional lexical information for the data item label;

use the search query to search a clue concept index to produce a search result, wherein the clue concept index comprises clue concept records that identify clues for concepts, and wherein the search result identifies clue concept records from the clue concept index that match the search query; and

select a concept from the clue concept records identified in the search result as the concept for the data item.

16. The computer program product of claim 15 , wherein:

the additional lexical information for the data item label comprises tokens, wherein each token in the tokens comprises text from the data item label and a lemma of the text from the data item label; and

wherein the program instructions are executable by the device to cause the device to build the search query using the text and lemma from each token in the tokens for the data item label.

17. The computer program product of claim 15 , wherein the search result comprises a relevance score for each clue concept record identified in the search result, and wherein the program instructions are executable by the device to cause the device to:

modify the relevance score for each clue concept record identified in the search result based on which field in the clue concept record is matched to the search query in the search to produce a boosted relevance score for each clue concept record identified in the search result; and

use the boosted relevance score to select the concept from the clue concept records identified in the search result as the concept for the data item.

18. The computer program product of claim 17 , wherein the program instructions are executable by the device to cause the device to:

determine a weighted relevance score for each clue concept record identified in the search result by weighting the boosted relevance score for each clue concept record identified in the search result with a weight determined by a scoring algorithm; and

select concepts in the clue concept record with the highest weighted relevance score as concepts for the data item.

19. The computer program product of claim 15 , wherein the program instructions are executable by the device to cause the device to:

identify a language of the data item label;

determine whether the language of the data item label is English;

in response to a determination that the language of the data item label is not English, use the search query to search a language-specific clue concept index for the language of the data item label and an English clue concept index;

in response to a determination that the language of the data item label is English, use the search query to search only an English clue concept index.

20. The computer program product of claim 15 , wherein the program instructions are executable by the device to cause the device to generate the clue concept index by:

receiving concept labels for concepts;

automatically generating lexical clues from the concept labels;

analyzing the lexical clues using a natural language processing tool to generate additional lexical information for the concepts;

generating the clue concept records using the lexical clues and additional lexical information as clues for the concepts; and

incorporating the clue concept records in the clue concept index.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 31, 2019
From: VALD, MARGARITA; SHEFFER, YARON; RESHEFF, YEHEZKEL SHRAGA; BARENHOLZ, TZVIKA
To: INTUIT INC.
Reel/Frame 051390/0798 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 21, 2019
From: SUN, CHANGYING; LUO, LIN; WILLS, GRAHAM
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 050780/0102 →
Continuity (1)
Related Publication 20210117505A1 · Apr 22, 2021