Readability awareness in natural language processing systems

Patent metadata

Field	Value
Publication number	US-10242092-B2
Application number	US-201715794282-A
Country	US
Kind code	B2
Filing date	Oct 26, 2017
Priority date	Jan 5, 2016
Publication date	Mar 26, 2019
Grant date	Mar 26, 2019

Abstract

Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

First claim

What is claimed is:

1. A method for electronic natural language processing in an electronic natural language processing (NLP) system, comprising:
receiving an electronic text input;
determining a readability level indicator of the electronic text input, wherein the readability level indicator comprises at least one of a grammatical error, a slang term, and a misspelling type in the electronic text input;
comparing the readability level indicator of the electronic text input with readability indicators of one or more questions in a corpus of questions; and
determining a readability level of the electronic text input based on the readability level indicator and further based on readability levels of the one or more questions.

2. The method of claim 1 , further comprising:
receiving the electronic text input from an electronic input source in response to an input from a user.

3. The method of claim 1 , further comprising:
identifying the electronic text input as a question.

4. The method of claim 3 , further comprising:
generating a plurality of candidate answers for the question; and
selecting a set of candidate answers from among the plurality of candidate answers based on matching readability levels of the set of candidate answers to the readability level of the question.

5. The method of claim 1 , further comprising:
parsing the electronic text input using a full parsing process.

6. The method of claim 1 , wherein identifying at least one of a slang term and a misspelling in the electronic text input comprises identifying, in the electronic text input, one or more of:
an abbreviation of a word corresponding to an acronym associated with the word in a collection of text messaging acronyms; and
a misspelling of the word, wherein the misspelling corresponds to a phonetic reading of the word.

7. The method of claim 1 , further comprising:
training a natural language processing model based on the results of the comparison.

8. A computer system for electronic natural language processing, comprising:
one or more computer devices each having one or more processors and one or more tangible storage devices; and
a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for:
receiving an electronic text input;
determining a readability level indicator of the electronic text input, wherein the readability level indicator comprises at least one of a grammatical error, a slang term, and a misspelling type in the electronic text input;
comparing the readability level indicator of the electronic text input with readability indicators of one or more questions in a corpus of questions; and
determining a readability level of the electronic text input based on the readability level indicator and further based on readability levels of the one or more questions.

9. The system of claim 8 , wherein instructions for identifying at least one of a slang term and a misspelling in the electronic text input comprise instructions for identifying, in the electronic text input, one or more of:
an abbreviation of a word corresponding to an acronym associated with the word in a collection of text messaging acronyms; and
a misspelling of the word, wherein the misspelling corresponds to a phonetic reading of the word.

10. The system of claim 8 , wherein the program instructions further comprise instructions for:
receiving the electronic text input from an electronic input source in response to an input from a user.

11. The system of claim 8 , wherein the program instructions further comprise instructions for:
identifying the electronic text input as a question.

12. The system of claim 8 , wherein the program instructions further comprise instructions for:
generating a plurality of candidate answers for the question; and
selecting a set of candidate answers from among the plurality of candidate answers based on matching readability levels of the set of candidate answers to the readability level of the question.

13. The system of claim 8 , wherein the program instructions further comprise instructions for:
parsing the electronic text input using a full parsing process.

14. The system of claim 8 , wherein the program instructions further comprise instructions for:
training a natural language processing model based on the results of the comparison.

15. A computer program product for electronic natural language processing, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising:
receiving an electronic text input, by the computer;
determining, by the computer, a readability level indicator of the electronic text input, wherein the readability level indicator comprises at least one of a grammatical error, a slang term, and a misspelling type in the electronic text input;
comparing, by the computer, the readability level indicator of the electronic text input with readability indicators of one or more questions in a corpus of questions; and
determining, by the computer, a readability level of the electronic text input based on the readability level indicator and further based on readability levels of the one or more questions.

16. The computer program product of claim 15 , wherein the method further comprises:
identifying, by the computer, the electronic text input as a question.

17. The computer program product of claim 15 , wherein the method further comprises:
generating, by the computer, a plurality of candidate answers for the question; and
selecting, by the computer, a set of candidate answers from among the plurality of candidate answers based on matching readability levels of the set of candidate answers to the readability level of the question.

18. The computer program product of claim 15 , wherein the method further comprises:
receiving the electronic text input, by the computer, from an electronic input source in response to an input from a user.

Assignees

• IBM

Inventors

• Byron Donna K
• Goyal Devendra
• Krishnamurthy Lakshminarayanan
• Santos Moraes Priscilla
• Smith Michael C

Classifications

• G06F40/40

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title

• G06F40/253

  Grammatical analysis; Style critique · CPC title

• G06F40/205

  Parsing · CPC title

• G06F40/232 Primary

  Orthographic correction, e.g. spell checking or vowelisation · CPC title

• G06F16/345

  Summarisation for human users · CPC title