Semantic Encoding for Compressed Text Search on Low-Power Devices
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Solution Overview
Problem
Current text analysis processes, particularly in information processing apparatuses with limited computation resources like mobile terminals, face high loads due to the need for decompressing and associating compressed documents and semantic analysis results, which are not efficiently handled by existing compression dictionaries that lack word-specific information.
Innovation Solution
An encoding device and method that performs morphological and semantic analyses in advance, storing results in a compressed state, and uses a shared dictionary for words in compression dictionaries to associate each word with its semantic analysis result, reducing process loads by eliminating the need for decompression and association processes during utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compressed documents and semantic analysis results are stored separately using existing compression dictionaries, then data compression is achieved, but high process loads occur during utilization due to required decompression and association operations
Solution Approach 1:
The patent combines compressed documents and semantic analysis results into a single integrated data structure where semantic information is embedded within the compressed data. This merging eliminates the need for separate decompression and association operations during utilization, as the semantic analysis results are directly accessible from the compressed data without requiring separate processing steps.
Solution Approach 2:
The patent performs semantic analysis during the compression process itself, rather than requiring separate post-compression analysis. By conducting the semantic analysis in advance and integrating the results into the compressed data structure, the system prepares all necessary information beforehand, eliminating the need for additional analysis operations during utilization and reducing process loads on devices with limited computation resources.
2Measurement precision
If semantic analysis is performed during utilization of compressed documents, then accurate semantic understanding is achieved, but calculation costs increase significantly on devices with limited computation resources
Solution Approach 1:
The patent performs semantic analysis during the compression process on a server or powerful processing system, and embeds the analysis results within the compressed data structure. When the compressed data is utilized on mobile terminals or devices with limited computation resources, the semantic analysis is already complete and the results are directly available, eliminating the need to perform computationally expensive semantic analysis operations on the resource-constrained device while maintaining high semantic understanding accuracy.
3Device complexity
If compression dictionaries lack word-specific information, then compression simplicity is maintained, but efficient association between words and semantic analysis results cannot be achieved
Solution Approach 1:
The patent enhances the compression dictionary structure by incorporating word-specific information such as part-of-speech tags and other lexical characteristics directly into the compressed data structure. This local enhancement allows each word instance to carry its specific semantic properties, enabling efficient association between words and their corresponding semantic analysis results without requiring complex external dictionaries or multiple lookup operations, thus improving association efficiency while maintaining reasonable structural complexity.
Data Source
AI summary
A recording medium having stored therein an encoding program that causes a computer to execute a process, the process including first generating a plurality of word codes by assigning a compression code to each of a plurality of words contained in a sentence in a compression target document, second generating a plurality of pieces of semantic structure information respectively corresponding to the plurality of words by performing a semantic analysis of the sentence, third generating a plurality of semantic structure codes by assigning each of the plurality of compression codes to corresponding semantic structure information, and outputting the plurality of word codes and the plurality of semantic structure codes with a specific order.