Automatic Crowd Sourcing for Machine Learning in Information Extraction

• US 20130066818A1
• Filed: 09/12/2012
• Published: 03/14/2013
• Est. Priority Date: 09/13/2011
• Status: Abandoned Application

• Current Assignee: EXB Asset Management GmbH
• Original Assignee: EXB Asset Management GmbH
• Inventors: Assadollahi, Ramin, Bordag, Stefan
• Application Number:
  US13/611,831
• Publication Number:
  US 20130066818A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models

Abstract

A method for enabling machine learning from unstructured documents is described. The method comprises analyzing at an electronic device, one or more structured databases, thereby providing a mapping between a plurality of referenced character strings and a corresponding plurality of type labels; providing, at the electronic device, a first unstructured document comprising a plurality of unstructured character strings; analyzing the first unstructured document to identify a first character string of the plurality of unstructured character strings which is associated with a first referenced character string of the plurality of referenced character strings; associating, within the first unstructured document, a first type label which is mapped to the first referenced character string to the first character string; and determining a training set for machine learning from the first unstructured document comprising the association to the first type label.

Claims

1. A method for enabling machine learning from unstructured documents, the method comprising

analyzing, at an electronic device, one or more structured databases, thereby providing a mapping between a plurality of referenced character strings and a corresponding plurality of type labels;

providing, at the electronic device, a first unstructured document comprising a plurality of unstructured character strings;

analyzing the first unstructured document to identify a first character string of the plurality of unstructured character strings which is associated with a first referenced character string of the plurality of referenced character strings;

annotating, within the first unstructured document, the first character string with a first type label which is mapped to the first referenced character string; and

determining a training set for machine learning from the first unstructured document comprising the annotation with the first type label.

2. The method of claims 1, further comprising

replacing the first character string within the first unstructured document by the first type label;

and

determining the training set from the first unstructured document comprising the first type label instead of the first character string.

3. The method of claim 1, further comprising:

transmitting the training set to a central machine learning server.

4. The method of claim 3, further comprising:

determining, at the central machine learning server, a pattern from the training set.

5. The method of claim 4, wherein the pattern is a syntactic rule defining a syntactic relationship between one or more type labels and/or one or more character strings;

and wherein the syntactic rule comprises one or more functional elements which define a function which is performed on the one or more character strings.

6. The method of claim 5, wherein the function performed on the one or more character strings is any of:

a similarity function applied to a character string, indicating that the syntactic rule applies to variants of a pre-determined degree of similarity to the character string to which the similarity function is applied; and

an OR function performed on a plurality of character strings, indicating that the syntactic rule applies to any one or more of the plurality of character strings.

7. The method of claim 4, further comprising:

transmitting the pattern to the electronic device;

providing, at the electronic device, a second unstructured document;

applying the pattern and the plurality of referenced character strings to the second unstructured document to determine a new referenced character string and a corresponding type label; and

storing the new referenced character string within the one or more structured databases in accordance to its corresponding type label, thereby yielding an extended plurality of referenced character strings.

8. The method of claim 7, further comprising

determining a frequency of occurrence of the new referenced character string within the second unstructured document;

storing the new referenced character string within the one or more structured databases only if the frequency of occurrence exceeds a pre-determined threshold value.

9. The method of claim 7, further comprising

repeating the applying step using the extended plurality of referenced character strings.

10. The method of claim 7, further comprising:

iterating the determining of the training set at the electronic device, the transmitting of the training set to the central machine learning server, the determining of the pattern at the central machine learning server and the transmitting of the pattern to the electronic device, thereby enabling adaptive machine learning.

11. The method of claim 1, wherein the electronic device is a personal computing device, e.g. any one of:

a smartphone, a notebook, a desktop PC, a mobile telephone, a tablet PC.

12. The method of claim 1, wherein the one or more structured databases are any of:

an address book database, with the plurality of type labels representing one or more of;

surname, first name, street name, house number, city name, city code, state name, country name, telephone number, email address;

a calendar database, with the plurality of type labels representing one or more of;

date, time, year, month, day, hour, minute, appointment, meeting, birthday, anniversary;

a task database, with the plurality of type labels representing one or more of;

task, date, time, priority;

a file structure, with the plurality of type labels representing one or more of;

folder name, file name, storage drive, URL.

13. The method of claim 1, wherein the first unstructured document is any one or more of:

an Email message;

a text document in a computer readable format;

a short message server, SMS, message.

14. A system configured for enabling machine learning from unstructured documents, the system comprising an electronic device configured to

analyze one or more structured databases, thereby providing a mapping between a plurality of referenced character strings and a corresponding plurality of type labels;

provide a first unstructured document comprising a plurality of unstructured character strings;

analyze the first unstructured document to identify a first plurality of character strings of the plurality of unstructured character strings which is associated with a first plurality of referenced character strings of the plurality of referenced character strings;

associate, within the first unstructured document, the first plurality of type labels which is mapped to the first plurality of referenced character strings to the corresponding first plurality of character strings;

determine a training set for machine learning from the first unstructured document comprising the association to the first type label.

15. The system of claim 14, further comprising

a plurality of electronic devices configured to determined a corresponding plurality of training sets, and configured to transmit the plurality of training sets to a central machine learning server;

the central machine learning server configured to determine one or more patterns from the plurality of training sets.