Text Complexity Assessment via N-gram Segmentation
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Solution Overview
Problem
Existing text complexity measurement algorithms lack subtlety and accuracy, particularly in assessing language-specific complexity and often produce wildly different results due to superficial metrics like word length and syllable count, leading to inaccurate comparisons across languages.
Innovation Solution
A computer-operated natural language processing system processes text into sub-blocks of predetermined sequence sizes, compares these to reference bases for language identification, and calculates an average complexity score using a quantitative assessment engine, providing a readability score that accounts for the complexity and scarcity of words in the broader language context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional text complexity algorithms (FLESCH-KINDCAID, ARI, SMOG) are used that measure based on syllables and letter counts, then the implementation is simple and computationally efficient, but the measurement precision and accuracy of text complexity assessment deteriorates
Solution Approach 1:
The patent segments text into n-grams (sub-blocks of text in predetermined sequence sizes) and processes each segment individually. This allows the system to capture contextual relationships and language-specific patterns while maintaining computational efficiency through modular processing of discrete text units.
Solution Approach 2:
The patent changes the measurement parameters from simple syllable/letter counts to complexity scores based on word matching against language-specific reference bases. The system determines an average complexity score for each n-gram by comparing against reference bases associated with different natural languages, thereby improving measurement precision while adapting to language-specific characteristics.
2Measurement precision
If language-specific reference bases are compared to identify source language, then the measurement precision and language-specific accuracy improves, but the device complexity and computational overhead increases
Solution Approach 1:
The patent performs preliminary language identification by comparing processed text against multiple language-specific reference bases before conducting the main complexity assessment. This preliminary action determines which reference base has the highest number of matching words, thereby identifying the source language and enabling subsequent language-appropriate complexity scoring without requiring complex real-time language detection during measurement.
3Measurement precision
If n-gram processing with language-specific reference bases is implemented, then the text complexity assessment accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent processes text in partial segments (n-grams of predetermined sequence sizes) rather than analyzing the entire text body at once. This allows the system to determine average complexity scores for each n-gram independently and aggregate them, reducing the computational burden per processing unit while maintaining overall assessment accuracy through cumulative results.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for automated quantitative assessment of text complexity. A system may include processing at least one body of text in a text-based query using a natural language processing engine. The processed text may include sub-blocks of text in a predetermined sequence size such as an n-gram. The system may compare reference bases to the processed text, where each reference base is associated with a different natural language. The system determines which of the reference bases has a highest number of matching words within the body of text, and thereby identifies the reference base as the source language of the supplied text. The system then determines an average complexity score for n-gram using a quantitative assessment engine. The system then applies a readability score to the body of text based on the average complexity scores of the n-grams.