N-gram Language Model for Multi-word Text Correction
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Solution Overview
Problem
Existing text input methods on electronic devices suffer from inaccuracies due to small virtual keyboards and incomplete text recognition, limiting communication speed and accuracy, especially when entering phrases or groups of words, with no predictive capabilities for multiple words or phrases.
Innovation Solution
The implementation of an n-gram language model that corrects and predicts multiple words or phrases by assigning probabilities based on previous words, allowing for auto-completion and auto-correction of entire sentences and paragraphs, with dynamic adjustments to user-specific vocabulary and language translation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual keyboards are used for text input, then text entry is enabled on mobile devices, but the small size results in selection of wrong characters and reduced accuracy
Solution Approach 1:
The system performs preliminary actions by predicting and generating candidate corrections for potential typing errors before the user completes the input. The error correction system proactively analyzes partial inputs and prepares correction suggestions in advance, allowing users to confirm or reject corrections without manually correcting each error.
Solution Approach 2:
The system implements feedback mechanisms by providing real-time correction suggestions and confidence scores to users. The error correction system analyzes user inputs, generates probability-based corrections, and feeds back multiple candidate suggestions with confidence levels, allowing users to select the intended word even when the initial input is incorrect.
2Measurement precision
If text recognition software is used to translate spoken words into text, then text entry is enabled, but inaccuracies occur due to variances in speech quality and voice tone
Solution Approach 1:
The system applies parameter changes by adjusting the n-gram model parameters and correction thresholds based on the confidence scores and error patterns detected in speech-to-text conversions. The error correction system dynamically modifies correction aggressiveness and candidate generation parameters to accommodate variations in speech quality while maintaining accuracy.
3Productivity
If traditional n-gram models are used for auto-completion, then single word predictions are improved, but no predictive capability exists for groups of words or phrases
Solution Approach 1:
The system transitions from single-word n-gram predictions to phrase-level predictions by extending the n-gram model to operate on sequences of words rather than isolated tokens. This dimensional extension allows the system to predict and complete entire phrases and sentences based on contextual patterns, significantly improving text entry productivity for multi-word inputs.
4Reliability
If auto-correction functionality is provided for single words, then typing errors are reduced, but corrections are limited to the current word being entered
Solution Approach 1:
The error correction system implements universality by providing multi-functional correction capabilities that operate at multiple levels: single-word correction, phrase-level correction, and sentence-level correction. The system can identify and correct errors spanning multiple words simultaneously, making the correction functionality applicable to diverse error patterns beyond isolated typos.
Data Source
AI summary
An input text string is received that contains characters or words. The input text string can be completed or corrected using contact scores based on n-grams. In addition, a subsequent text string and a preceding text string for the input text string are also identified, again using n-gram scores. A corrected text string is created by inserting the preceding text string before the input text string and appending the subsequent text string after the input text string.