SYSTEM AND METHOD FOR AUTOMATIC QUESTION ANSWERING

• US 20160247068A1
• Filed: 05/02/2016
• Published: 08/25/2016
• Est. Priority Date: 11/01/2013
• Status: Abandoned Application

• Current Assignee: Tencent Technology Company Limited (Tencent Holdings Limited)
• Original Assignee: Tencent Technology Company Limited (Tencent Holdings Limited)
• Inventors: LIN, FEN
• Application Number:
  US15/144,373
• Publication Number:
  US 20160247068A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A system and method for automatic question answering is provided. The system includes: a user inputting module configured to receive question information; a question analyzing module configured to analyze the question information, and determine a set of keywords, a question type and a user intention type corresponding to the question information; a syntax retrieving and ranking module configured to retrieve, in a question and answer library and a category tree, answer candidates based on the question information, the set of keywords, the question type and the user intention type, determine a retrieval relevance between each of the answer candidates and the question information and rank the answer candidates according to the retrieval relevance, each of the answer candidates having a sequence number; and an outputting module configured to output an answer candidate ranked with a specified sequence number. By using the application, lower costs for collection and improve successful rate of answers are realized by the system for automatic question answering.

Claims

1. A system for automatic question answering, comprising:

a user inputting module configured to receive question information;

a question analyzing module configured to analyze the question information, and determine a set of keywords, a question type and a user intention type corresponding to the question information;

a syntax retrieving and ranking module configured to retrieve, in a question and answer library and a category tree, answer candidates based on the question information, the set of keywords, the question type and the user intention type, determine a retrieval relevance between each of the answer candidates and the question information, and rank the answer candidates according to the retrieval relevance, each of the answer candidates having a sequence number; and

an outputting module configured to output one of the answer candidates ranked with a specified sequence number.

2. The system according to claim 1, wherein the question analyzing module comprises:

a word segmenting module configured to process the question information by word segmentation or part-of-speech tagging, and obtain a processing result;

a keywords determining module configured to determine a set of keywords, according to the processing result;

a question type analyzing module configured to determine the question type, according to the set of keywords; and

a user intention analyzing module configured to determine the user intention type, according to the set of keywords and a stored user model.

3. The system according to claim 2, wherein the keywords determining module is further configured to indentify entity words from the processing result of the word segmenting module, obtain core words from the entity words, expand the core words to obtain expansion words, and output the core words and the expansion words as the set of keywords.

4. The system according to claim 1, wherein the syntax retrieving and ranking module comprises:

a question and answer library retrieving module configured to retrieve, in the question and answer library, answer candidates matching the set of keywords and calculate a question and answer library retrieval relevance between each of the answer candidates and the question information;

a category tree retrieving module configured to retrieve, in the category tree, answer candidates matching the question information, the set of keywords and the user intention type, according to preset template settings and model settings, and calculate the category tree retrieval relevance between each of the answer candidates and the question information; and

an answers ranking module, configured to calculate a total relevance between each of the answer candidates and the question information based on the question and answer library retrieval relevance and the category tree retrieval relevance, and rank the answer candidates according to the total relevance.

5. The system according to claim 4, wherein the answers ranking module is further configured to:

determine whether an answer form of one of the answer candidates is a specified form; and

if the answer form of one of the answer candidates is the specified form, increase the total relevance of the answer candidate.

6. The system according to claim 4, wherein the answers ranking module is further configured to:

acquire, in stored user models, user type information of the user proposing the question information, wherein the user type information indicating a user type of the user, determine whether an answer type of one of the answer candidates is consistent with the user type; and

if the answer type of one of the answer candidates is consistent with the user type, increase the total relevance of the answer candidate.

7. The system according to claim 4, wherein the answers ranking module is further configured to:

determine whether question type of one of the answer candidates is consistent with the question type determined by the question analyzing module; and

if the question type of one of the answer candidates is consistent with the question type determined by the question analyzing module, increase the total relevance of the answer candidate.

8. The system according to any one of claims 1, wherein the system further comprises a voice recognizing module, which is configured to, when the question information is voice information, recognize the voice information and output the recognized result to the question analyzing module.

9. A method for automatic question answering, comprising:

receiving question information;

analyzing the question information, and determining a set of keywords, a question type and a user intention type corresponding to the question information;

retrieving, in a question and answer library and a category tree, answer candidates based on the question information, the set of keywords, the question type and the user intention type;

determining a retrieval relevance between each of the answer candidates and the question information;

ranking the answer candidates according to the retrieval relevance, each of the answer candidates having a sequence number; and

outputting one of the answer candidate ranked with a specified sequence number.

10. The method according to claim 9, wherein the analyzing step further comprises following steps:

processing the question information by word segmentation or part-of-speech tagging, and obtaining a processing result;

determining a set of keywords according to the processing result;

determining the question type according to the set of keywords; and

determining the user intention type according to the set of keywords and a stored user model.

11. The method according to claim 10, wherein the step of determining the set of keywords further comprises following steps:

indentifying entity words from the processing result of the word segmentation and/or part-of-speech tagging;

obtaining core words from the entity words;

expanding the core words to obtain expansion words; and

outputting the core words and the expansion words as the set of keywords.

12. The method according to claim 9, wherein the retrieving step comprise following steps:

retrieving, in the question and answer library, answer candidates matching the set of keywords and calculating a question and answer library retrieval relevance between each of the answer candidates and the question information;

retrieving, in the category tree, answer candidates matching the question information, the set of keywords and the user intention type according to template settings and model settings, and calculating a category tree retrieval relevance between each of the answer candidates and the question information; and

calculating a total relevance between each of the answer candidates and the question information based on the question and answer library retrieval relevance and the category tree retrieval relevance, and ranking the answer candidates according to the total relevance.

13. The method according to claim 12, wherein the method further comprises the following steps:

determining whether an answer form of one of the answer candidates is a specified form; and

if the answer form of one of the answer candidates is the specified form, increasing the total relevance of the answer candidate.

14. The method according to claim 12, wherein the method further comprises the following steps:

acquiring, in stored user models, user type information of the user proposing the question information, wherein the user type information indicating a user type of the user;

determining whether an answer type of one of the answer candidates is consistent with the user type; and

if the answer type of one of the answer candidates is consistent with the user type, increasing the total relevance of the answer candidate.

15. The method according to claim 12, wherein the method further comprises the following steps:

determining whether question type of one of the answer candidates is consistent with the question type determined by the analyzing step; and

if the question type of one of the answer candidates is consistent with the question type determined by the analyzing step, increasing the total relevance of the answer candidate.

16. The method according to claim 12, wherein the step of calculating the total relevance of the answer candidates comprises:

calculating the total relevance of the answer candidates according to Equation 1;

p(x)=α

.sim(x)+β

.match(x)+θ

.voice(x)+δ

.user(x)+σ

.type(x),

wherein, p(x) denotes the total relevance of current answer candidate;

sim(x) denotes the question and answer library retrieval relevance of the answer candidate to the question information, and regarding retrieval results from the category tree, sim(x) is 0;

match(x) denotes the category tree retrieval relevance of the answer candidate to the question information, and regarding retrieval results from the question and answer library, match(x) is 0;

voice(x) indicates whether an answer form of the answer candidate is voice form, and if the answer form is voice form, voice(x) is 1, and otherwise voice(x) is 0;

user(x) indicates whether an answer type of the answer candidate is consistent with a user type in user models, and if the answer type is consistent with the user type in user models, user(x) is 1, and otherwise user(x) is 0;

type(x) indicates whether the answer type of the answer candidate meets the analyzed question type, and if the answer type meets the analyzed question type, type(x) is 1, and otherwise type(x) is 0; and

wherein parameters meet 1>

α

>

β

>

δ

>

θ

>

σ

>

0.

17. The method according to any one of claims 9, wherein before analyzing the question information, the method further comprises:

when the question information is voice information, recognizing the voice information and generating text information, and analyzing the text information to determine the set of keywords, the question type and the user intention type.