Mistyped Word Correction via Machine Learning Segmentation
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Solution Overview
Problem
Current methods for correcting mistyped words due to inaccurate keyboard inputs or wrong keyboard layouts are inefficient, often requiring extensive computational resources and failing to accurately identify and correct errors without distinguishing between the two sources of mistyping.
Innovation Solution
A machine learning algorithm is trained using a corpus of text to generate and rank realistic mistypes, allowing it to correct mistyped words by determining the likelihood of character substitutions based on co-occurrence and keyboard layout analysis, without the need to identify the specific cause of the mistype.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to correct mistyped words by building comprehensive databases of mistyped and correct word pairs, then correction coverage is improved, but computational resources and energy consumption increase significantly
Solution Approach 1:
The invention segments the correction task into two distinct analysis paths: one for detecting wrong keyboard layout usage and another for correcting inaccurate keyboard inputs. Each segment uses specialized algorithms optimized for its specific purpose, avoiding the need to process all possible mistype scenarios through a single comprehensive database, thereby reducing computational overhead while maintaining correction coverage.
Solution Approach 2:
The system performs preliminary analysis to determine the cause of the mistype before applying correction. By first identifying whether the error stems from wrong keyboard layout or inaccurate input, the system can apply targeted correction strategies, avoiding unnecessary computational effort associated with exhaustive database searching and enabling more efficient energy utilization.
2Device complexity
If a single comprehensive correction method is used for all mistype causes, then device complexity is reduced, but measurement precision of error source identification deteriorates
Solution Approach 1:
The invention implements a dynamic correction system that adapts its approach based on the detected error type. The system automatically switches between different correction algorithms depending on whether wrong keyboard layout or inaccurate input is identified, allowing it to maintain high precision without requiring a static, overly complex comprehensive method for all scenarios.
Solution Approach 2:
The system changes its operational parameters based on the error source. When wrong keyboard layout is detected, it applies layout-specific correction rules; when inaccurate input is detected, it uses proximity-based correction. This parameter adaptation allows the system to maintain simplicity in each mode while achieving high precision through context-aware selection.
3Manufacturing precision
If extensive databases of mistyped words are built to improve correction accuracy, then manufacturing precision of correction results is improved, but loss of time for database building and processing increases
Solution Approach 1:
The invention extracts only the essential features needed for correction from comprehensive databases, focusing on keyboard layout configurations and character proximity relationships. By taking out only the critical data elements rather than processing entire word databases, the system maintains high correction accuracy while significantly reducing the time required for data processing.
Solution Approach 2:
The system applies partial action by using specialized, targeted correction algorithms for each error type rather than exhaustive database searching. For wrong keyboard layout errors, it uses layout-mapping algorithms; for inaccurate inputs, it uses proximity-based suggestions. This partial approach achieves sufficient accuracy without the excessive time cost of comprehensive database processing.
Data Source
AI summary
A method and system for correcting a mistyped word resulting from at least one of an inaccurate keyboard input and wrong keyboard layout is disclosed. The method comprises selecting a first n-characters of a first word, selecting candidate characters that are likely subsequent characters to the first n-characters by calculating a candidate score value for each candidate character; generating a respective positive reformulated word for each selected candidate characters which corresponds to the first word having replaced the n+1 character with the respective candidate character; and training a machine learning algorithm (MLA) to determine a set of features representing a property between each reformulated words and the first word; and training the MLA to determine the reformulated word having an absence of a mistype.