Utilizing regular expression embeddings for named entity recognition systems

Patent metadata

Field	Value
Publication number	US-12443597-B2
Application number	US-202418587716-A
Country	US
Kind code	B2
Filing date	Feb 26, 2024
Priority date	Aug 23, 2019
Publication date	Oct 14, 2025
Grant date	Oct 14, 2025

Abstract

Various embodiments are directed to a system that utilizes regular expression (regex) to recognize at least portions of characters, words, text, numbers, etc. in a structured or unstructured dataset, any patterns associated therewith, and/or similarities between the determined patterns. In examples, a regex-based pattern recognition platform may receive a dataset and determine whether at least a first regex pattern and a second regex pattern can be identified. The occurrences of the first and second regex patterns and the frequency of those occurrences may reveal something about the dataset itself or any patterns contained therein.

First claim

What is claimed is:

1. A device, comprising:
a memory; and
a processor coupled with the memory to:
determine a frequency of occurrences of a first regex pattern of a regex list in a dataset;
create a vector, the vector specifying a vector position and a regex value for at least the first regex pattern;
adjust the regex value corresponding to the first regex pattern by addition of a predefined value for each occurrence of the first regex pattern in the dataset;
detect a set of false matches between the first regex pattern of the regex list and the dataset;
adjust, based on the set of false matches, the regex list via a machine learning model or a classification model; and
provide the vector to at least one entity recognition system.

2. The device of claim 1 , wherein the processor, when generating the theme, is further operable to:
determine whether the dataset contains a second regex pattern of the regex list;
determine a number of occurrences of the second regex pattern in the dataset; and
increment a regex value corresponding to the second regex pattern by the predefined value for each occurrence of the second regex pattern in the dataset.

3. The device of claim 1 , the processor further operable to perform a normalization on the vector.

4. The device of claim 1 , the processor further operable to identify personally identifiable information (PII) in the dataset and replacing the PII with a regex pattern from the regex list corresponding to the PII.

5. The device of claim 1 , wherein the predefined value is one.

6. The device of claim 1 , the processor further operable to:
provide a normalized vector to the machine learning model or the classification model, and wherein the normalized vector is used to train the machine learning model or the classification model.

7. The device of claim 1 , wherein the at least one entity recognition system determines whether (i) one or more portions of the dataset includes personally identifiable information (PII) or (ii) classifies the one or more portions of the dataset as PII.

8. The device of claim 1 , wherein the first regex pattern defines a personally identifiable information (PII) pattern.

9. A method, comprising:
determining, via one or more processors, whether a dataset contains a first regex pattern of a regex list;
determining, via the one or more processors, a number of occurrences of the first regex pattern in the dataset;
generating, via the one or more processors, a vector, the vector specifying a vector position and a regex value for at least the first regex pattern;
incrementing, via the one or more processors, the regex value corresponding to the first regex pattern by a predefined value for each occurrence of the first regex pattern in the dataset;
determining, via the one or more processors, a set of false matches between the first regex pattern of the regex list and the dataset;
refining, based on the set of false matches, the regex list via a machine learning model or a classification model; and
providing, via the one or more processors, the vector to at least one entity recognition system.

10. The method of claim 9 , further comprising:
determining, via the one or more processors, whether the dataset contains a second regex pattern of the regex list;
determining, via the one or more processors, a number of occurrences of the second regex pattern in the dataset; and
incrementing, via the one or more processors, a regex value corresponding to the second regex pattern by the predefined value for each occurrence of the second regex pattern in the dataset.

11. The method of claim 9 , further comprising performing a normalization on the vector.

12. The method of claim 9 , further comprising identifying personally identifiable information (PII) in the dataset and replacing the PII with a regex pattern from the regex list corresponding to the PII.

13. The method of claim 9 , wherein the predefined value is one.

14. The method of claim 9 , further comprising providing a normalized vector to the machine learning model or the classification model, and wherein the normalized vector is used to train the machine learning model or the classification model.

15. The method of claim 9 , wherein the at least one entity recognition system determines whether (i) one or more portions of the dataset includes personally identifiable information (PII) or (ii) classifies the one or more portions of the dataset as PII.

16. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to:
determine, via the processor, a number of occurrences of a first regex pattern of a regex list in a dataset;
generate, via the processor, a vector, the vector specifying a vector position and a regex value for at least the first regex pattern;
increment, via the processor, the regex value corresponding to the first regex pattern by a predefined value for each occurrence of the first regex pattern in the dataset;
determine, via the processor, a set of false matches between the first regex pattern of the regex list and the dataset;
train, based on the set of false matches, a machine learning model or a classification model, via the regex list; and
provide, via the processor, the vector to at least one entity recognition system.

17. The non-transitory computer-readable storage medium of claim 16 , wherein the instructions to process the search query cause the processor to:
determine, via the one or more processors, whether the dataset contains a second regex pattern of the regex list;
determine, via the one or more processors, a number of occurrences of the second regex pattern in the dataset; and
increment, via the one or more processors, a regex value corresponding to the second regex pattern by the predefined value for each occurrence of the second regex pattern in the dataset.

18. The non-transitory computer-readable storage medium of claim 16 , wherein the instructions further cause the processor to perform a normalization on the vector.

19. The non-transitory computer-readable storage medium of claim 16 , wherein the instructions further cause the processor to identify personally identifiable information (PII) in the dataset and replacing the PII with a regex pattern from the regex list corresponding to the PII.

20. The non-transitory computer-readable storage medium of claim 16 , wherein the instructions further cause the processor to provide a normalized vector to the machine learning model or the classification model, and wherein the normalized vector is used to train the machine learning model or the classification model.

Assignees

• Capital One Services Llc

Inventors

• Goodsitt Jeremy Edward
• Walters Austin Grant
• Farivar Reza
• Watson Mark Louis
• Truong Anh
• Rafferty Galen
• Pham Vincent

Classifications

• G06F16/35 Primary

  Clustering; Classification · CPC title

• G06F16/374

  Thesaurus · CPC title

• G06F16/3329

  Natural language query formulation · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N20/20

  Ensemble learning · CPC title