Text Search Stack Frame Logic for Keyword Exclusion
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Solution Overview
Problem
Conventional text search methods, such as morphological analysis and Ngram methods, fail to accurately search for texts containing specific keywords while excluding similar or related keywords, leading to inefficient processing and high memory usage due to the need for extensive dictionary definitions and address holding.
Innovation Solution
A search method that acquires a search condition character string and an out-of-search-condition character string, creating stack frames to match character strings in the text, pushing correspondences for hits and changing them to non-correspondences for exclusions, allowing for rapid text search without analyzing the text structure or holding keyword addresses, thus reducing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the Ngram method is used to search for texts, then dictionary definitions are not required and processing is simplified, but texts containing similar keywords (e.g., Tokyo-to when searching for Kyoto) are erroneously searched and exclusion conditions cannot be properly applied
Solution Approach 1:
The patent segments the text into character strings of N characters (Ngrams) and processes each segment independently. This allows the system to maintain simplicity while improving accuracy by checking multiple overlapping segments rather than treating the entire text as a single unit, thereby distinguishing between similar keywords more effectively
Solution Approach 2:
The patent introduces a new dimension by holding addresses (positions) of matched keywords and applying exclusion conditions at the address level rather than just at the keyword level. This additional dimensional approach allows precise control over which texts are included or excluded from search results
2Measurement precision
If address holding and exclusion condition confirmation are implemented to compensate for Ngram method disadvantages, then search accuracy improves, but memory consumption increases and processing speed decreases
Solution Approach 1:
The patent performs preliminary action by pre-defining the relationship between search condition character strings and out-of-search-condition character strings before actual text search. This preprocessing step creates a lookup structure that speeds up the main search operation, reducing the computational burden during text processing while maintaining high accuracy
3Measurement precision
If the morphological analysis method is used, then search precision improves by properly identifying keywords, but a large load is imposed on dictionary definition work and uncommon words cannot be searched
Solution Approach 1:
The patent uses inexpensive, easily modifiable N-gram patterns instead of complex, hard-to-maintain morphological analysis dictionaries. The N-gram approach requires no extensive dictionary definitions and can adapt to new words and phrases simply by adjusting the N-gram parameters, making the system both simpler and more flexible
Data Source
AI summary
A search device creates as many stack frames as the number obtained by adding one to the number of search condition character strings contained in an out-of-search-condition character string in a stack, sequentially inputs character strings in a text into automaton data, determines whether the character strings in the text hit the search condition character string or the out-of-search-condition character string to push correspondence to the stack or to change correspondence into non-correspondence, and determines whether the text is to be searched.