Information Processing for Semantic Factor Extraction From Text

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Solution Overview

Problem

Existing solutions fail to effectively interpret and utilize the rich information contained in unstructured text about objects, such as user comments and reviews, limiting the extraction of factors affecting the object and their quantification.

Innovation Solution

A method and system for extracting keywords from unstructured text, grouping them semantically, and determining target factors representing aspects of the object, combined with structured factors to identify key factors that influence the object, optimizing information collection forms for improved evaluation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If unstructured text is used to extract information about a target object, then rich information is available, but the information cannot be effectively interpreted and utilized

Engineering Contradiction:

Improverich informationVSAvoidinterpretation and utilization

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The unstructured text is segmented into discrete keywords through extraction, transforming continuous text into discrete units that can be systematically processed. This segmentation enables the rich information to be organized and utilized effectively by breaking down the text into manageable keyword elements that can be grouped and analyzed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Keywords serve as an intermediary between the unstructured text and the structured factors. The keyword extraction and grouping process creates a bridge that translates unstructured textual information into a format that can be systematically compared and integrated with structured evaluation factors, enabling effective interpretation and utilization of the rich information.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If keywords are extracted and grouped from unstructured text, then new factors affecting the object can be discovered, but the process adds complexity to information processing

Engineering Contradiction:

Improvediscovery of new factorsVSAvoidinformation processing

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The complex task of discovering new factors from unstructured text is segmented into manageable steps: keyword extraction, semantic grouping, and factor determination. This segmentation reduces processing complexity by breaking down the overall task into discrete, systematic operations that can be executed sequentially.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs self-service by automatically extracting keywords, grouping them semantically, and determining target factors without requiring manual intervention. This automated approach reduces processing complexity by eliminating the need for manual text analysis while still enabling the discovery of new factors affecting the target object.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If target factors are determined from keyword grouping, then accuracy of information collection is enhanced, but the processing time increases

Engineering Contradiction:

Improveaccuracy of information collectionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

Keywords are extracted and grouped in advance before the actual factor determination process. This preliminary action prepares the data structure ahead of time, enabling faster and more accurate factor determination when needed, as the foundational work of text analysis and keyword organization has already been completed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The manual process of analyzing unstructured text and identifying factors is replaced with an automated computational system that uses keyword extraction and semantic grouping algorithms. This substitution dramatically reduces processing time while maintaining or improving accuracy, as the automated system can process text much faster than manual analysis while systematically identifying relevant factors.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12499315B2Method, apparatus, device and storage medium for information processing

Publication Date: 2025.12.16 NEC CORP

AI summary

According to the embodiments of the present disclosure, a method, apparatus, device, and storage media for information processing are provided. The method comprises extracting a plurality of keywords from an unstructured text set for a target object. The method comprises grouping at least a portion of the plurality of keywords based on semantics of the plurality of keywords. The method further comprises determining a target factor corresponding to a group of keywords based on a result of the grouping. The target factor represents an aspect of the target object. As such, new factors that affect the target object can be identified.