METHODS AND SYSTEMS FOR AUTOMATED TEXT CORRECTION

• US 20170177563A1
• Filed: 03/06/2017
• Published: 06/22/2017
• Est. Priority Date: 09/24/2010
• Status: Abandoned Application

• Current Assignee: National University of Singapore
• Original Assignee: National University of Singapore
• Inventors: Dahlmeier, Daniel Hermann Richard, Ng, Hwee Tou
• Application Number:
  US15/451,387
• Publication Number:
  US 20170177563A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs

Abstract

The present embodiments demonstrate systems and methods for automated text correction. In certain embodiments, the methods and systems may be implemented through analysis according to a single text correction model. In a particular embodiment, the single text correction model may be generated through analysis of both a corpus of learner text and a corpus of non-learner text.

Claims

1. An apparatus, comprising:

at least one processor and a memory device coupled to the at least one processor, in which the at least one processor is configured;

to receive a natural language text input, the text input comprising a grammatical error in which a portion of the input text comprises a class from a set of classes;

to generate a plurality of selection tasks from a corpus of non-learner text that is assumed to be free of grammatical errors, wherein for each selection task a classifier re-predicts a class used in the non-learner text;

to generate a plurality of correction tasks from a corpus of learner text, wherein for each correction task a classifier proposes a class used in the learner text;

to train a grammar correction model using a set of binary classification problems that include the plurality of selection tasks and the plurality of correction tasks; and

to use the trained grammar correction model to predict a class for the text input from the set of possible classes.

2. The apparatus of claim 1, in which the at least one processor is further configured to outputting a suggestion to change the class of the text input to the predicted class if the predicted class is different than the class in the text input.

3. The apparatus of claim 1, wherein the learner text is annotated by a teacher with an assumed correct class.

4. The apparatus of claim 1, wherein the class is a preposition associated with a prepositional phrase in the input text, and wherein the at least one processor is further configured to extract feature functions for the classifiers from prepositional phrases in the non-learner text and the learner text.

5. The apparatus of claim 1, wherein the class is a preposition associated with a prepositional phrase in the input text, and wherein the at least one processor is further configured to extract feature functions for the classifiers from prepositional phrases in the non-learner text and the learner text.

6. The apparatus of claim 1, wherein the non-learner text and the learner text have a different feature space, the feature space of the learner text including the word used by a writer.

7. The apparatus of claim 1, wherein training the grammar correction model comprises minimizing a loss function on the training data.

8. The apparatus of claim 1, wherein training the grammar correction model further comprises identifying a plurality of linear classifiers through analysis of the non-learner text, and wherein the linear classifiers further comprise a weight factor included in a matrix of weight factors, and wherein training the grammar correction model further comprises performing a Singular Value Decomposition (SVD) on the matrix of weight factors.

9. A non-transitory tangible computer-readable medium comprising computer-readable code that, when executed by a computer, cause the computer:

to receive a natural language text input, the text input comprising a grammatical error in which a portion of the input text comprises a class from a set of classes;

to generate a plurality of selection tasks from a corpus of non-learner text that is assumed to be free of grammatical errors, wherein for each selection task a classifier re-predicts a class used in the non-learner text;

to generate a plurality of correction tasks from a corpus of learner text, wherein for each correction task a classifier proposes a class used in the learner text;

to train a grammar correction model using a set of binary classification problems that include the plurality of selection tasks and the plurality of correction tasks; and

to use the trained grammar correction model to predict a class for the text input from the set of possible classes.

10. The non-transitory tangible computer-readable medium of claim 9, wherein the computer-readable code further comprises computer-readable code that cause the computer to output a suggestion to change the class of the text input to the predicted class if the predicted class is different than the class in the text input.

11. The non-transitory tangible computer-readable medium of claim 9, wherein the learner text is annotated by a teacher with an assumed correct class.

12. The non-transitory tangible computer-readable medium of claim 9, wherein the class is an article associated with a noun phrase in the input text, and wherein the computer-readable code further comprises computer-readable code that cause the computer to extract feature functions for the classifiers from noun phrases in the non-learner text and the learner text.

13. The non-transitory tangible computer-readable medium of claim 9, wherein the class is a preposition associated with a prepositional phrase in the input text, and wherein the computer-readable code further comprises computer-readable code that cause the computer to extract feature functions for the classifiers from prepositional phrases in the non-learner text and the learner text.

14. The non-transitory tangible computer-readable medium of claim 9, wherein the non-learner text and the learner text have a different feature space, the feature space of the learner text including the word used by a writer.

15. The non-transitory tangible computer-readable medium of claim 9, wherein training the grammar correction model comprises minimizing a loss function on the training data.

16. The non-transitory tangible computer-readable medium of claim 9, wherein training the grammar correction model further comprises identifying a plurality of linear classifiers through analysis of the non-learner text, and wherein the linear classifiers further comprise a weight factor included in a matrix of weight factors, and wherein training the grammar correction model further comprises performing a Singular Value Decomposition (SVD) on the matrix of weight factors.