Readability awareness in natural language processing systems

Patent metadata

Field	Value
Publication number	US-9875300-B2
Application number	US-201615162641-A
Country	US
Kind code	B2
Filing date	May 24, 2016
Priority date	Jan 5, 2016
Publication date	Jan 23, 2018
Grant date	Jan 23, 2018

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 a plurality of natural language documents;
determining readability level indicators in the plurality of natural language documents;
receiving a query text;
assigning a score to the query text based on at least a misspelling type, wherein the misspelling type comprises one or more of:
a misspelling in a word falling within a defined range;
a misspelling of a word, where the word is found in at least one dictionary, and not found in at least another dictionary; and
a number of auto-corrections detected during an input process for the query text, the input process comprising receiving the query text from a user via an input device, and

providing, in response to receiving the query text, at least one natural language document whose readability level is within a threshold distance of a readability level of the query text, wherein the readability level of the query text is based on one or more readability level indicators including at least one of a grammatical error, a slang term, and a misspelling type in the query text.

2. The method of claim 1 , further comprising:
training a data model based on determining the readability level for the one or more of the plurality of natural language documents.

3. The method of claim 1 , wherein further comprising:
receiving an electronic text input from a user;
querying, based on the electronic text input, a database storing the plurality of natural language documents; and
retrieving a set of candidate answers in response to the query, wherein a candidate answer comprises at least a portion of a natural language document.

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

5. The method of claim 3 , wherein the NLP system comprises a question-answering (QA) pipeline having a plurality of processing stages, wherein one or more of steps of the method are performed by one or more of the plurality of processing stages, the method further comprising:
filtering one or more natural language documents, by at least one processing stage, to exclude one or more natural language documents from processing by at least one other processing stage.

6. The method of claim 3 , wherein retrieving a set of candidate answers in response to the query comprises:
defining a score function having as an input at least a readability level, wherein the set of candidate answers comprise natural language documents whose score meets a threshold value.

7. The method of claim 1 , wherein determining a readability level for one or more of the plurality of natural language documents based on respective readability level indicators comprises:
determining a readability level for at least two portions of at least one natural language document.

Assignees

• IBM

Inventors

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

Classifications

• G06F40/232

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

• G06F40/205

  Parsing · CPC title

• G06F40/40

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

• G06F40/253 Primary

  Grammatical analysis; Style critique · CPC title

• G06F16/345

  Summarisation for human users · CPC title