Essay Scoring Device Using Context Segmentation and Vector Similarity
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Solution Overview
Problem
Conventional college admissions consulting services fail to adequately evaluate essays beyond grammar and vocabulary, neglecting important factors such as theme relevance, interest to universities, and holistic analysis, especially in light of changes in SAT exam policies.
Innovation Solution
An electronic device with a preprocessing unit to segment input stories by words, distinguish context information, and a scoring unit to calculate similarity based on word distributions, linguistic forms, and analogous words, providing a holistic scoring method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional editing services focus only on grammar and vocabulary, then the editing process is simple and quick, but the evaluation fails to capture theme relevance, university interest, and holistic quality
Solution Approach 1:
The essay analysis system segments the essay into multiple analysis elements (theme, relevance, interest level, grammar, vocabulary) and processes each element separately through dedicated processing units. This allows comprehensive evaluation without overwhelming complexity by breaking down the holistic analysis into manageable segments.
Solution Approach 2:
The analysis system is designed to perform multiple functions simultaneously - grammatical analysis, thematic analysis, relevance assessment, and interest level evaluation - all within a single integrated system. This multi-functional approach enables comprehensive essay evaluation without requiring multiple separate tools.
2Measurement precision
If holistic analysis of essays is implemented, then evaluation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary segmentation of the essay into analysis elements before detailed analysis. By pre-identifying theme, relevance, and grammatical components, the system prepares the data structure for efficient subsequent processing, reducing overall computation time while maintaining comprehensive analysis.
Solution Approach 2:
The system transforms textual essay data into numerical parameters and vectors that can be processed efficiently by computational algorithms. By converting qualitative linguistic features into quantifiable parameters, the system enables fast mathematical operations while preserving the nuance of holistic analysis.
3Measurement precision
If multiple linguistic forms and analogous words are analyzed, then word distribution accuracy improves, but the complexity of word processing increases
Solution Approach 1:
The system introduces vector representations as an intermediary between raw text and analysis results. By converting words and linguistic forms into vector spaces, the system handles multiple linguistic forms and analogous words through mathematical operations on vectors, simplifying the processing complexity while maintaining high accuracy in word distribution analysis.
Data Source
AI summary
An electronic device, according to one embodiment of the present disclosure, may comprise: a preprocessing unit which segments an inputted story by word and, according to a word meaning inference possibility, distinguishes first context information and second context information and outputs same; a valid word selection unit which extracts, from the first context information, word information satisfying a predetermined criterion, extracts, among the first context information, word information having a specific linguistic form, and carries out expansion into similar words for the second context information; and a scoring unit which receives output values, of the valid word selection unit, for respective analysis elements of essay text data, calculates the degree of similarity between data being compared and the output values, and thus outputs scoring information of the essay text data.