Output Candidate Suggestion Using Corpus-Based Seed Word Matching
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Solution Overview
Problem
Conventional input-assistance functions lack the ability to suggest sophisticated or academic words and phrases that convey vague or ambiguous ideas, and fail to create unique sentences with a particular style or taste, especially in conversations or writings that require refined vocabulary.
Innovation Solution
A computer-implemented method that suggests output candidates by selecting a corpus similar to the user input, identifying a seed word in the input, and extracting entries from a dictionary with high similarity, allowing for the suggestion of more academic or sophisticated phrases based on the user's vocabulary level and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional input-assistance functions (learning functions, prediction functions, spelling correction, kana-to-kanji conversion) are used, then basic input support is provided, but the ability to suggest sophisticated or academic words and phrases is lacking
Solution Approach 1:
The patent introduces a corpus as an intermediary between the user input and the dictionary. The corpus contains pre-processed linguistic data that bridges the gap between basic input assistance and sophisticated word suggestions. By selecting a corpus similar to the user input and using it to guide dictionary entry selection, the system can suggest academic or sophisticated words without requiring direct complex analysis of all possible vocabulary options.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and organizing linguistic data into corpuses before they are needed for suggestion. The corpus is prepared in advance with structured information about word usage, contexts, and relationships. This preliminary organization allows the system to quickly suggest sophisticated words during actual use without performing complex real-time analysis.
2Measurement precision
If a corpus similar to user input is selected and dictionary entries are identified with high similarity, then sophisticated and academic word suggestions are enabled, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by selecting a specific corpus that is similar to the user input rather than searching through all possible linguistic data. This localized selection focuses computational resources on the most relevant data subset. Additionally, the system identifies only dictionary entries that have high similarity to the seed word found in the user input, rather than processing all dictionary entries uniformly.
3Adaptability or versatility
If multiple corpuses are maintained for different vocabulary levels and styles, then the ability to match corpus style to user input is improved, but device complexity and data management burden increase
Solution Approach 1:
The patent makes the corpus selection mechanism universal by designing it to automatically identify and select the most appropriate corpus based on similarity to user input. Rather than requiring manual configuration or complex classification systems, the same corpus selection mechanism works for different vocabulary levels and styles by comparing linguistic features. This multi-functional approach allows the system to handle diverse input types with a single unified process.
Data Source
AI summary
The present invention may be a method, a computer system, and a computer program product for suggesting an output candidate. The method comprises receiving a user input; selecting a corpus containing an expression similar to the user input among a plurality of corpuses; finding, in the user input, a seed word that may be present in a definition statement of an entry in a dictionary; identifying, in the dictionary, an entry of a definition statement containing the seed word or within a threshold similarity to the seed word with reference to the selected corpus; and suggesting the identified entry as an output candidate.