AUTOMATIC EXTRACTION USING MACHINE LEARNING BASED ROBUST STRUCTURAL EXTRACTORS

• US 20100223214A1
• Filed: 02/27/2009
• Published: 09/02/2010
• Est. Priority Date: 02/27/2009
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Kshirsagar, Meghana, Sengamedu, Srinivasan H., Kirpal, Alok S., Satpal, Sandeepkumar Bhuramal
• Application Number:
  US12/395,586
• Publication Number:
  US 20100223214A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/86 Mapping to a database

Abstract

A method and apparatus for automatically extracting information from a large number of documents through applying machine learning techniques and exploiting structural similarities among documents. A machine learning model is trained to have at least 50% accuracy. The trained machine learning model is used to identify information attributes in a sample of pages from a cluster of structurally similar documents. A structure-specific model of the cluster is created by compiling a list of top-K locations for each attribute identified by the trained machine learning model in the sample. These top-K lists are used to extract information from the pages of the cluster from which the sample of pages was taken.

Claims

1. A computer-implemented method comprising:

producing a trained machine learning model based at least in part on a plurality of documents;

applying the trained machine learning model to a set of documents;

based at least in part on the applying the trained machine learning model to the set of documents, determining a plurality of locations of a particular attribute in the set of documents;

associating a set of locations with the particular attribute, based at least in part on the plurality of locations; and

based at least in part on the set of locations, extracting, from a particular document, an attribute value corresponding to the particular attribute;

wherein the method is performed by one or more computing devices programmed to be special purpose machines pursuant to program instructions.

2. The computer-implemented method of claim 1,

wherein each document of the set of documents is structurally similar to each document of the balance of documents in the set of documents;

and

wherein the particular document is structurally similar to each document of the set of documents.

3. The computer-implemented method of claim 1,

wherein the trained machine learning model is at least one of (a) Conditional Random Field-based or (b) Hidden Markov model-based;

and

wherein the trained machine learning model has 50% or greater precision.

4. The computer-implemented method of claim 1, wherein a particular location of the set of locations comprises an XPath corresponding to at least one of (a) a leaf node of a Document Object Model (DOM) tree, and (b) a subtree of a Document Object Model (DOM) tree.

5. The computer-implemented method of claim 1, wherein associating the set of locations with the particular attribute further comprises:

determining a second set of locations comprising the locations included in the plurality of locations that are not included in the set of locations;

determining a first set of frequencies comprising a frequency with which each location in the set of locations occurs in the set of documents;

determining an aggregate frequency based at least in part on adding together each frequency of the first set of frequencies;

determining whether the aggregate frequency is above a pre-defined threshold;

wherein the pre-defined threshold is 90%; and

in response to determining that the aggregate frequency is not above the pre-defined threshold;

determining a second set of frequencies comprising a frequency with which each location in the second set of locations occurs in the set of documents;

identifying a particular location of the second set of locations having a highest frequency of the second set of frequencies; and

including the particular location in the set of locations.

6. The computer-implemented method of claim 1, wherein associating a set of locations with the particular attribute further comprises:

determining whether a frequency with which a particular location occurs in the set of documents is above a pre-defined threshold; and

in response to determining that the frequency is above the pre-defined threshold, including the particular location in the set of locations.

7. The computer-implemented method of claim 1, wherein extracting an attribute value corresponding to the particular attribute from a particular document based at least in part on the set of locations further comprises:

determining a particular location of the particular attribute in the particular document based at least in part on the set of locations; and

extracting the attribute value from the particular location in the particular document.

8. The computer-implemented method of claim 1, wherein extracting an attribute value corresponding to the particular attribute from a particular document based at least in part on the set of locations further comprises:

determining a first attribute value of the particular attribute based on applying the trained machine learning model to the particular document;

determining a second attribute value of the particular attribute based on the set of locations;

determining whether the first attribute value and the second attribute value are the same;

in response to determining that the first attribute value and the second attribute value are not the same, determining whether the set of documents is sufficiently representative of the particular document; and

in response to determining that the set of documents is sufficiently representative of the particular document, extracting the second attribute value.

9. The computer-implemented method of claim 1, wherein extracting an attribute value corresponding to the particular attribute from a particular document based at least in part on the set of locations further comprises:

determining a first attribute value of the particular attribute based on applying the trained machine learning model to the particular document;

determining a second attribute value of the particular attribute based on the set of locations;

determining whether the first attribute value and the second attribute value are the same;

in response to determining that the first attribute value and the second attribute value are not the same, determining whether the set of documents is sufficiently representative of the particular document; and

in response to determining that the set of documents is not sufficiently representative of the particular document, extracting no value.

10. The computer-implemented method of claim 1,

wherein applying the trained machine learning model to a set of documents further comprises extracting an attribute value for the particular attribute from a particular document of the set of documents;

and

wherein determining the plurality of locations of the particular attribute in each document of the set of documents further comprises;

determining a location of the attribute value in a DOM tree of the particular document; and

including the location in the plurality of locations.

11. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 1.

12. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 2.

13. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 3.

14. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 4.

15. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 5.

16. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 6.

17. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 7.

18. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 8.

19. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 9.

20. One or more storage media storing instructions which, when executed by one or more computing devices, cause performance of the method recited in claim 10.