DOCUMENT PROCESSOR AND ASSOCIATED METHOD

• US 20100114562A1
• Filed: 04/05/2007
• Published: 05/06/2010
• Est. Priority Date: 11/03/2006
• Status: Abandoned Application

• Current Assignee: Appen Pty Ltd. (Appen Ltd.)
• Original Assignee: Appen Pty Ltd. (Appen Ltd.)
• Inventors: Pham, Son Bao, Hutchinson, Ben, Gaustad, Tanja, Radford, Wil, Estival, Dominique
• Application Number:
  US12/513,099
• Publication Number:
  US 20100114562A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/131 Fragmentation of text files, e.g. creating reusable text-blocks; Linking to fragments, e.g. using XInclude; Namespaces
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]

Abstract

A computer implemented method of processing a digitally encoded document having a text composed by an author by using a processor to analyse the segmentation, punctuation and linguistics of text and storing the results in a digitally accessible format. Author traits are then predicted using a machine learning system based on the results of the segmentation, punctuation and linguistics analysis of the text.

Claims

1. A computer implemented method of processing a digitally encoded document having text composed by an author, said method including the steps of:

using a processor to analyse segmentation of the text and storing results of said segmentation analysis in a digitally accessible format;

using a processor to analyse punctuation of the text and storing results of said punctuation analysis in a digitally accessible format;

using a processor to linguistically analyse the text and storing results of said linguistic analysis in a digitally accessible format; and

predicting an author trait using a machine learning system that is adapted to receive the results of said linguistic analysis, said segmentation analysis and said punctuation analysis as input, said machine learning system having been trained to process said input so as to output at least one predicted author trait, wherein said at least one predicted author trait is a demographic trait.

2. A method according to claim 1 wherein said linguistic analysis includes identification of predefined words and phrases in the text.

3. A method according to claim 2 wherein said words and phrases include any one or more of the following types:

peoples'"'"' names, locations, dates, times, organizations, currency, uniform resource locators (URL'"'"'s), email addresses, addresses, organizational descriptors, phone numbers, typical greetings and/or typical farewells.

4. A method according to claim 3 further including the use of a database of words and phrases of any one or more of the following types:

peoples'"'"' names, locations, dates, times, organizations, currency, uniform resource locators (URL'"'"'s), email addresses, addresses, organizational descriptors, phone numbers, typical greetings and/or typical farewells.

5. A method according to claim 1 wherein the segmentation analysis includes an analysis of the paragraph segmentation used in the text.

6. A method according to claim 1 wherein the segmentation analysis includes an analysis of the sentence segmentation used in the text.

7. A method according to claim 1 wherein the results of said linguistic analysis, said segmentation analysis and said punctuation analysis are represented by one or more data structures associated with the document.

8. A method according to claim 7 wherein the data structures are feature vectors.

9. A method according to claim 1 wherein the machine learning system utilizes any one or more of the following techniques:

Support Vector Machines;

Naï

ve Bayes;

Decision Trees;

Lazy Learners;

Rule-based Learners;

Ensemble/meta-learners and/or

Maximum Entropy.

10. A method according to claim 1 wherein the machine learning system has been trained with reference to a representative sample of training documents and with reference to known author trait information associated with each of the training documents.

11. A method according to claim 1 including a step of processing the document to ascertain whether the document is in a preferred format and, if the document is not in the preferred format, converting at least some of the information within the document to the preferred format.

12. A method according to claim 1 wherein the document is, or includes, any one of:

an email;

text sourced from an email;

data sourced from a digital source;

text sourced from an online newsgroup discussion;

text sourced from a multiuser online chat session;

a digitized facsimile;

an SMS message;

text sourced from an instant messaging communication session;

a scanned document;

text sourced by means of optical character recognition;

text sourced from a file attached to an email;

text sourced from a digital file;

a word processor created file;

a text file;

or text sourced from a web site.

13. A method according to claim 1 wherein said demographic trait includes any one or more of:

age;

gender;

educational level;

native language;

country of origin and/or geographic region.

21. The method according to claim 1 where the steps are implemented using a computer-readable medium containing computer executable code for instructing a computer.

22. The method according to claim 1 where the steps are implemented using a downloadable or remotely executable file or combination of files containing computer executable code for instructing a computer.

23. The method according to claim 1 where the steps are implemented using a computing apparatus having a central processing unit, associated memory and storage devices, and input and output devices.

14. A computer implemented method of processing a digitally encoded document having text composed by an author, said method including the steps of:

using a processor to analyse segmentation of the text and storing results of said segmentation analysis in a digitally accessible format;

using a processor to analyse punctuation of the text and storing results of said punctuation analysis in a digitally accessible format;

using a processor to linguistically analyse the text and storing results of said linguistic analysis in a digitally accessible format; and

predicting an author trait using a machine learning system that is adapted to receive the results of said linguistic analysis, said segmentation analysis and said punctuation analysis as input, said machine learning system having been trained to process said input so as to output at least one predicted author trait, wherein said at least one predicted author trait is a psychometric trait.

15. A method according to claim 14 wherein said psychometric trait includes any one or more of:

extraversion;

agreeableness;

conscientiousness;

neuroticism;

psychoticism and/or openness.

16. A method according to claim 14 wherein said at least one predicted author trait is associated with a confidence level representing an estimate of the likelihood that the predicted trait is correct.

17. A method according to claim 14 wherein the document is parsed so as to distinguish author composed text from non-author composed text and wherein only author composed text is primarily used as the basis for the prediction of author traits.

18. A method of training a machine learning system, said method including:

compiling a representative sample of training documents, each training document being associated with known author trait information;

using a processor to linguistically analyse text of the training documents and storing the results of said linguistic analysis in a digitally accessible format;

using a processor to analyse segmentation of the text of the training documents and storing the results of said segmentation analysis in a digitally accessible format;

using a processor to analyse punctuation of the text of the training documents and storing the results of said punctuation analysis in a digitally accessible format; and

using the machine learning system in a training mode to process the results of said linguistic analysis, said segmentation analysis and said punctuation analysis, along with the associated known author trait information, so as to formulate a function for use by the machine learning system in an operational mode to process input documents so as to output at least one predicted author trait, wherein said at least one predicted author trait is a demographic trait and/or a psychometric trait.

19. A method according to claim 18 wherein at least some of said known author trait information is compiled by subjecting known authors to a questionnaire.

20. A method according to claim 19 wherein said questionnaire includes questions adapted to elicit answers relating to demographic and/or psychometric traits of the known authors.

24. A machine learning system for processing a digitally encoded document having text composed by an author, said machine learning system having been trained to process said document so as to output at least three of the following six predicted author traits:

age;

gender;

educational level;

native language;

country of origin and/or geographic region.

25. A machine learning system for processing a digitally encoded document having text composed by an author, said machine learning system having been trained to process said document so as to output at least three of the following six predicted author traits:

extraversion;

agreeableness;

conscientiousness;

neuroticism;

psychoticism and/or openness.