Text Matching Device Using Dependency Analysis for Problem Report Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for specifying and collecting problem reports and request messages on networks, such as social media, are limited to specific categories and fail to distinguish between problem reports and support information, leading to inefficiencies in matching relevant information during disasters or everyday situations.
Innovation Solution
A text matching and classification system using machine learning to categorize texts into problem reports, request messages, and support information based on morphemes, dependency relations, and predicate classifications, enabling accurate matching and retrieval of relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If information search services collect and display a large amount of information, then the quantity of information available to users increases, but the difficulty of finding relevant information increases
Solution Approach 1:
The patent segments information into distinct categories (problem reports, request messages, support information) using text classification. This segmentation allows users to navigate organized information types rather than searching through undifferentiated content, directly addressing the contradiction by maintaining information quantity while improving findability through structural organization.
Solution Approach 2:
The patent introduces text classification and text matching systems as intermediary mechanisms between users and information. These intermediaries automatically categorize and retrieve relevant information based on semantic understanding, reducing the manual effort required to find relevant content while preserving access to the full information corpus.
2Measurement precision
If text classification uses multiple features including morphemes, dependency information, and core combinations, then the precision of text classification improves, but the complexity of the classification system increases
Solution Approach 1:
The classification system segments the text analysis process into distinct feature extraction stages: morpheme-level features, dependency relation features, and core combination features. This segmentation allows each feature type to be processed independently and combined systematically, improving classification precision while managing complexity through modular organization of the analysis pipeline.
Solution Approach 2:
The patent employs multiple parameters (morphemes, dependency information, core combinations) to characterize texts. By changing and combining these parameters in a systematic way, the system achieves high classification precision. The complexity is managed by defining clear rules for parameter extraction and combination, transforming a potentially chaotic multi-parameter system into a structured analytical framework.
Data Source
AI summary
[Object] To provide a system for automatically and reliably collecting information belonging to a given category, and matching the information appropriately in a timely manner.[Solution] A text classifying device 30 analyzes dependency of collected texts by a morpheme analyzing unit 52 and a dependency analyzing unit 54. A problem report collecting unit 64 specifies a core consisting of noun+predicate in a text based on dependency relation of the text, and using a combination of noun classification (trouble/non-trouble) and predicate classification (excitatory/inhibitory), classifies the text to a problem report or the rest, by a method referred to as core-based matrix. Support information collecting device 66 and request message collecting device 68 collect support information and request messages in the similar manner. A matching device 76 matches problem reports and support information collected by problem report collecting unit 64 and support information collecting device 66 by a method referred to as co-occurrence core matrix, and thus associates problem information (support information) with appropriate support information (problem information).