ATTRIBUTE FILL USING TEXT EXTRACTION

• US 20150378975A1
• Filed: 06/25/2014
• Published: 12/31/2015
• Est. Priority Date: 06/25/2014
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Patnaik, Debprakash, Wu, Jianhui, Xu, Taibai, Zhang, Wenke, Shen, Rongzhou, Viswanathan, Krishnamurthy Koduvayur, Parent, Gabriel, Cao, Jiwei
• Granted Patent: US 10,102,195 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/174 Form filling; Merging
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

Systems and methods involve filling missing attribute values from unstructured text. A computing device may provide a plurality of items, such as an item catalog for an electronic marketplace. When an item is found to have a missing attribute value, a plurality of existing values for that attribute is compiled by mining other items. Text associated with the item is parsed to determine possible values for the attribute. From those possible values, the most likely value is identified and the missing attribute value is populated with that value.

Claims

1. A computer-implemented method, comprising:

identifying, for a first item of a plurality of items, at least one category associated with the first item;

determining, by a computer system, a plurality of attributes common to the identified at least one category;

identifying at least one attribute of the plurality of attributes that is not populated for the first item;

extracting, from at least one second item of the plurality of items, a plurality of existing values associated with the at least one attribute of the plurality of attributes;

identifying, from text associated with the first item, a plurality of candidate values, the plurality of candidate values comprising at least one candidate value for the at least one attribute of the plurality of attributes;

filtering, based at least in part on the plurality of existing values associated with the at least one attribute of the plurality of attributes, the plurality of candidate values to determine a likely value; and

populating, by the computing system, the at least one attribute of the plurality of attributes of the first item with the likely value.

2. The computer-implemented method of claim 1, Wherein the plurality of attributes common to the identified category is determined by extracting attributes from multiple second items of the plurality of items, each of the multiple second items associated with the identified category.

3. The computer-implemented method of claim 1, wherein the text associated with the item is unstructured.

4. The computer-implemented method of claim 1, further comprising determining a unit type that is associated with the at least one attribute of the plurality of attributes.

5. The computer-implemented method of claim 1, further comprising normalizing the at least one candidate value.

6. The computer-implemented method of claim 1, further comprising determining a range of existing values, the range of existing values being determined by identifying a minimum value and a maximum value in the plurality of existing values associated with the at least one attribute of the plurality of attributes.

7. The computer-implemented method of claim 6, wherein at least one candidate value is removed from the plurality of candidate values, based at least in part on the candidate value being outside of the range of existing values.

8. The computer-implemented method of claim 1, further comprising associating a set of priority indicators with the plurality of candidate values, wherein the appropriate value for the attribute is determined based at least in part on the set of priority indicators.

9. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform operations comprising:

identifying at least one first item of a plurality of items for which an attribute is unpopulated;

extracting a plurality of existing values for the attribute based at least in part on at least one second item of the plurality of items, the at least one second item having the attribute populated;

determining, based at least in part on the plurality of existing values, an appropriate value for the attribute from text associated with the at least one first item; and

populating the attribute of the at least one first item of the plurality of items with the determined appropriate value.

10. The non-transitory computer readable storage medium of claim 9, further comprising associating a set of priority indicators with the plurality of existing values, wherein the appropriate value for the attribute is determined based at least in part on the set of priority indicators.

11. The non-transitory computer readable storage medium of claim 9, wherein the at least one first item is related to the at least one second item.

12. The non-transitory computer readable storage medium of claim 9, wherein the text associated with the at least one first item is one of a title, comments or user review for the first item.

13. The non-transitory computer readable storage medium of claim 9, wherein the appropriate value is determined to be a phrase from the text associated with the at least one first item that most commonly occurs in the plurality of existing values.

14. The non-transitory computer readable storage medium of claim 9, wherein the appropriate value is determined by applying a rule from a rule set.

15. The non-transitory computer readable storage medium of claim 14, wherein the rule set is built using at least one machine learning algorithm.

16. The non-transitory computer readable storage medium of claim 14, wherein the rule set is associated with a category type to which the at least one first item belongs.

17. A system, comprising:

a processor; and

a memory device including instructions that, when executed by the processor, cause the system to;

provide a first item categorization for a set of items;

determine at least one attribute associated with the first item categorization;

identify at least one first item of the set of items for which the associated at least one attribute is empty;

determine, from one or more second items of the set of items, a range of existing values for the attribute;

identify, from data associated with the first item, at least one potential value for the attribute based at least in part on the determined range of values for the attribute;

determine, from the at least one potential value for the attribute, a probable value; and

set the at least one attribute associated with the first item to the probable value.

18. The system of claim 17, wherein the probable value is the maximum of the at least one potential value for the attribute or the minimum of the at least one potential value for the attribute.

19. The system of claim 17, wherein the probable value is determined based at least in part on a context of the probable value in the data associated with the first item.

20. The system of claim 17, wherein the at least one first item belongs to a second item categorization.