Context-Sensitive Language Correction Using Internet Corpus
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Solution Overview
Problem
Current language correction systems for small keyboard devices are inadequate in providing effective spelling, misused word, and grammar corrections, especially on devices like handheld, mobile, and touch screen devices, due to limitations in keyboard size and user input accuracy.
Innovation Solution
A computer-assisted language correction system that includes an alternatives generator, a selector, and a correction generator, utilizing an internet corpus for context-based scoring to provide multiple alternatives for each word in a sentence, allowing for spelling, misused word, and grammar corrections without user intervention, and employing contextual feature-sequences to rank corrections based on frequency and importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If language correction systems are implemented on small keyboard devices, then text accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses an internet corpus as an intermediary data source to provide contextual language information. The system retrieves contextual feature sequences from the internet corpus to score and rank correction alternatives, enabling accurate language correction without embedding complex language models directly in the small keyboard device.
Solution Approach 2:
The language correction system is divided into separate functional modules: an alternatives generator that creates multiple correction options, a selector that ranks alternatives using contextual feature sequences from the internet corpus, and a correction generator that applies the selected corrections. This segmentation allows each module to perform a specific function efficiently.
2Measurement precision
If multiple correction alternatives are generated for each word, then correction accuracy is improved, but processing time increases
Solution Approach 1:
The system pre-generates multiple correction alternatives for each word before final selection. By creating all possible correction options in advance and then ranking them using contextual feature sequences, the system ensures accurate selection without requiring time-consuming comparisons during the correction application phase.
Solution Approach 2:
The system changes the parameter of alternative generation by creating multiple correction options for each word with different levels of modification (spelling, grammar, word choice). These alternatives are then scored using contextual feature sequences, allowing the system to efficiently select the best correction based on contextual relevance rather than exhaustive analysis.
3Reliability
If contextual feature-sequences are used for scoring corrections, then correction relevance is improved, but computational requirements increase
Solution Approach 1:
The internet corpus serves as an intermediary that pre-contains contextual feature sequences and language patterns. Instead of computing complex language models in real-time on the small keyboard device, the system retrieves pre-computed contextual information from the internet corpus to score correction alternatives, significantly reducing local computational requirements.
Solution Approach 2:
The system uses copied contextual feature sequences from the internet corpus rather than generating original language models. By copying and applying pre-existing contextual patterns to score correction alternatives, the system achieves high correction relevance with minimal computational energy on the device.
4Productivity
If automatic correction is provided without user intervention, then productivity is improved, but risk of incorrect correction increases
Solution Approach 1:
The system provides feedback by generating multiple correction alternatives with different scores based on contextual feature sequences. The highest-scoring alternative is selected for automatic correction, but the system can also present top alternatives to the user for verification. This feedback mechanism ensures that automatic corrections are based on the most contextually relevant options.
Solution Approach 2:
The system changes the parameter of correction selection by using contextual feature sequence scores to automatically determine the best correction. Instead of requiring user input for each correction, the system automatically selects corrections based on the scoring parameter, improving productivity while maintaining reliability through context-aware selection.
Data Source
AI summary
A computer-assisted language correction system particularly suitable for use with small keyboard devices including spelling correction functionality, misused word correction functionality and grammar correction functionality utilizing contextual feature-sequence functionality employing an interne corpus.