METHOD AND APPARATUS FOR PROCESSING SEMANTIC ANALYSIS RESULT BASED ON ARTIFICIAL INTELLIGENCE

• US 20180150455A1
• Filed: 06/29/2017
• Published: 05/31/2018
• Est. Priority Date: 11/30/2016
• Status: Active Grant

• Current Assignee: Baidu Online Network Technology (Beijing) Co., Ltd (Baidu Incorporated)
• Original Assignee: Baidu Online Network Technology (Beijing) Co., Ltd (Baidu Incorporated)
• Inventors: HU, Zejin, YAO, Shujie
• Granted Patent: US 10,191,900 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models
• CPC Class Codes: G06V 30/274 Syntactic or semantic context, e.g. balancing

Abstract

A method and an apparatus for processing a semantic analysis result based on AI are provided. With the method, the weight of the analysis text in the corresponding analysis result is determined according to the preset weight configuration information; the semantic confidence of the analysis result is detected via the pattern matching algorithm; the analysis type of the analysis texts is determined by the first classification model, and the field matching confidence of the analysis texts over the analysis result is determined by the second classification model; and then the analysis quality data of the analysis result is obtained according to the weights, the semantic confidence, the analysis type and the field matching confidence.

Claims

1. A method for processing a semantic analysis result based on artificial intelligence, comprising:

determining, by at least one computing device, weights of analysis texts in a corresponding analysis result according to preset weight configuration information;

detecting, by the at least one computing device, a semantic confidence of the analysis result via a pattern matching algorithm;

determining, by the at least one computing device, an analysis type of the analysis texts by a first classification model;

determining, by the at least one computing device, a field matching confidence of the analysis texts over the analysis result by a second classification model; and

obtaining, by the at least one computing device, analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence.

2. The method according to claim 1, wherein, determining, by at least one computing device, weights of analysis texts in a corresponding analysis result according to preset weight configuration information comprises:

obtaining, by the at least one computing device, a field type of each of the analysis texts;

determine, by the at least one computing device, a weight of the each of the analysis texts in the corresponding analysis result according to preset weight configuration information corresponding to the field type.

3. The method according to claim 1, wherein, obtaining, by the at least one computing device, analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence comprises:

normalizing, by the at least one computing device, the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset function model to obtain normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence;

summing up, by the at least one computing device, the normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence so as to obtain the analysis quality data of the analysis result.

4. The method according to claim 1, wherein, obtaining, by the at least one computing device, analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence comprises:

performing, by the at least one computing device, a weighted calculation to obtain the analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset weighted calculation model.

5. The method according to claim 1, wherein,

comparing, by the at least one computing device, analysis quality data of a plurality of analysis results to a preset quality threshold;

deleting, by the at least one computing device, analysis results that cannot reach to the quality threshold;

sorting, by the at least one computing device, analysis results that reach to the threshold; and

feeding, by the at least one computing device, back the analysis results to the user according to a sorting result.

6. An apparatus for processing a semantic analysis result based on artificial intelligence, comprising:

a processor; and

a memory, configured to store instructions executable by the processor, wherein the processor is configured to;

determine weights of analysis texts in a corresponding analysis result according to preset weight configuration information;

detect a semantic confidence of the analysis result via a pattern matching algorithm;

determine an analysis type of the analysis texts by a first classification model;

determine a field matching confidence of the analysis texts over the analysis result by a second classification model;

obtain analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence.

7. The apparatus according to claim 6, wherein the processor is configured to determine weights of analysis texts in a corresponding analysis result according to preset weight configuration information by acts of:

obtaining a field type of each of the analysis texts;

determine a weight of the each of the analysis texts in the corresponding analysis result according to preset weight configuration information corresponding to the field type.

8. The apparatus according to claim 6, wherein the processor is configured to obtain analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence by acts of:

normalizing, the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset function model to obtain normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence;

summing up the normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence so as to obtain the analysis quality data of the analysis result.

9. The apparatus according to claim 6, wherein the processor is configured to obtain analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence by acts of:

performing a weighted calculation to obtain the analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset weighted calculation model.

10. The apparatus according to claim 6, wherein the processor is further configured to:

compare analysis quality data of a plurality of analysis results to a preset quality threshold;

delete analysis results that cannot reach to the quality threshold;

sort analysis results that reach to the threshold; and

feed back the analysis results to the user according to a sorting result.

11. A non-transitory computer readable storage medium, with instructions stored, wherein the instructions are executed by a processor to achieve a method for processing a semantic analysis result based on artificial intelligence, and the method comprises:

determining weights of analysis texts in a corresponding analysis result according to preset weight configuration information;

detecting a semantic confidence of the analysis result via a pattern matching algorithm;

determining an analysis type of the analysis texts by a first classification model;

determining a field matching confidence of the analysis texts over the analysis result by a second classification model;

obtaining analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence.

12. The non-transitory computer readable storage medium according to claim 11, wherein determining weights of analysis texts in a corresponding analysis result according to preset weight configuration information comprises:

obtaining a field type of each of the analysis texts;

determine a weight of the each of the analysis texts in the corresponding analysis result according to preset weight configuration information corresponding to the field type.

13. The non-transitory computer readable storage medium according to claim 11, wherein obtaining analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence comprises:

normalizing the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset function model to obtain normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence;

summing up the normalized results of the weights, the semantic confidence, the analysis type, and the field matching confidence so as to obtain the analysis quality data of the analysis result.

14. The non-transitory computer readable storage medium according to claim 11, wherein obtaining analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence comprises:

performing a weighted calculation to obtain the analysis quality data of the analysis result according to the weights, the semantic confidence, the analysis type, and the field matching confidence via a preset weighted calculation model.

15. The non-transitory computer readable storage medium according to claim 11, wherein the method further comprises:

comparing analysis quality data of a plurality of analysis results to a preset quality threshold;

deleting analysis results that cannot reach to the quality threshold;

sorting analysis results that reach to the threshold; and

feeding back the analysis results to the user according to a sorting result.