Profound Text Extraction Using Topic and Linguistic Analysis
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Solution Overview
Problem
Current methods fail to effectively extract profound text that conveys knowledge about attention targets like persons, content, and thoughts from large volumes of documents, despite efforts in statistical natural language processing.
Innovation Solution
An information processing apparatus and method that includes a collection unit, topic analysis unit, language analysis unit, evaluation setting unit, and profound text extraction unit to identify and extract sentences with unique expression patterns relevant to attention targets, using topic analysis, linguistic analysis, and machine learning to determine the profundity and relevance of extracted text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If statistical natural language processing is performed on a huge amount of documents, then knowledge about attention targets can be obtained, but the ability to extract profound text with distinguishing expression patterns is insufficient
Solution Approach 1:
The patent segments the document processing into multiple stages: collecting documents, performing topic analysis to identify local topics, detecting unique expression patterns through linguistic analysis, and extracting profound text based on both topic relevance and expression pattern uniqueness. This segmentation allows the system to handle large document volumes while maintaining high extraction quality by processing documents through multiple specialized analysis layers.
Solution Approach 2:
The patent applies local quality by focusing on specific local topics within the broader document corpus and identifying unique expression patterns specific to each local topic. Rather than treating all documents uniformly, the system analyzes documents according to their specific local topic characteristics and extraction criteria, thereby improving the quality of profound text extraction for each local topic while processing the entire large corpus.
2Productivity
If documents are searched and summarized, then information about attention targets is obtained, but profound text with distinguishing expression patterns cannot be extracted
Solution Approach 1:
The patent performs preliminary actions by first collecting documents, then performing topic analysis to pre-identify local topics and their relevance before extracting profound text. The linguistic analysis to detect unique expression patterns is also performed as a preliminary step. This preliminary processing enables the system to quickly identify and extract profound text with distinguishing expression patterns without requiring slow, manual verification, thereby maintaining high productivity while improving extraction accuracy.
3Measurement precision
If multiple analysis units are introduced to improve extraction quality, then profound text can be extracted accurately, but system complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing analysis units that serve multiple purposes. For example, the topic analysis unit not only identifies local topics but also determines document relevance to those topics. The linguistic analysis unit both detects unique expression patterns and evaluates their significance for profound text extraction. This multi-functionality reduces the need for separate dedicated units for each function, thereby improving extraction quality while controlling system complexity.
Solution Approach 2:
The patent merges multiple analysis functions into integrated units. The topic analysis and linguistic analysis are combined in a coordinated manner where the output of one unit feeds into the other. The profound text extraction unit combines topic relevance assessment and expression pattern detection into a single extraction process. This merging of functions achieves high extraction quality while minimizing the number of separate components and overall system complexity.
Data Source
AI summary
An information processing apparatus performs topic analysis on one or more collected documents to calculate a probability indicating the degree of fitness of each sentence constituting the collected document for each item of a local topic, performs linguistic analysis on the collected document to detect a unique expression pattern in each item of the local topic, sets topic usefulness for each sentence constituting the collected document on the basis of evaluation of the sentence by an evaluator, sets a total evaluation value with respect to each item of the local topic on the basis of the topic analysis result and the topic usefulness, selects an item of the local topic on the basis of the total evaluation values, and extracts an appropriate sentence for a unique expression pattern in the selected item of the local topic from the collected document as a profound text candidate.