Text Amendment System Using N-gram Frequency Analysis
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Solution Overview
Problem
Existing text processing technologies face challenges in accurately identifying and correcting errors, particularly in domain-specific and newly coined terminology, which can lead to incorrect classifications and missed opportunities due to inaccuracies introduced by speech-to-text techniques.
Innovation Solution
The method involves using reference text from the same domain and control text to identify and amend errors by processing n-grams, determining their frequencies, and applying a match policy that considers embeddings, phonetic information, and frequencies to replace potentially incorrect terms with more accurate ones, leveraging machine learning techniques for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech-to-text techniques are used to convert spoken language into text, then text processing speed and automation are improved, but errors and inaccuracies are introduced particularly in domain-specific and newly coined terminology
Solution Approach 1:
The patent introduces an intermediary text amendment system that acts as a mediator between speech-to-text conversion and final text processing. This system uses n-gram frequency analysis comparing domain-specific reference text against general control text to identify and correct inaccuracies in transcribed text, particularly for domain-specific and newly coined terms that speech-to-text systems struggle with
Solution Approach 2:
The patent implements a feedback mechanism where the text amendment system continuously learns from corrections. By comparing n-gram frequencies between domain reference text and control text, the system identifies discrepancies and amends the transcribed text accordingly, creating a feedback loop that improves accuracy while maintaining automated processing speed
2Measurement precision
If automated text correction is implemented, then text accuracy is improved, but system complexity increases due to multiple text processing steps
Solution Approach 1:
The patent segments the text correction task into distinct components: identifying n-grams in the transcribed text, comparing frequencies against domain reference text and control text, determining candidate corrections, and applying amendments. This segmentation allows each component to be processed independently and efficiently, managing complexity through modular design
Solution Approach 2:
The patent uses copying by creating n-gram representations from the transcribed text and comparing them against n-grams extracted from domain reference text and control text. This copying approach allows frequency analysis without requiring complex direct text manipulation, simplifying the correction process while maintaining accuracy
3Measurement precision
If domain-specific reference text is used for correction, then correction accuracy for domain terms is improved, but processing time increases due to additional text analysis
Solution Approach 1:
The patent applies preliminary action by pre-processing the domain reference text and control text to extract and store n-gram frequencies before the actual correction process. This pre-computation of statistical data allows the correction system to quickly compare and identify discrepancies without performing complex analysis during the time-critical correction phase
Solution Approach 2:
The patent replaces complex mechanical text comparison operations with statistical frequency analysis. Instead of performing intricate semantic analysis or rule-based matching during correction, the system substitutes these with efficient n-gram frequency comparisons against pre-computed statistics from domain reference text, significantly reducing processing time while maintaining high accuracy
Data Source
AI summary
Aspects of the present disclosure provide techniques for automated text amendment. Embodiments include identifying a first plurality of n-grams in first text associated with a domain. Embodiments include identifying a second plurality of n-grams in second text associated with the domain. Embodiments include identifying a third plurality of n-grams in third text that is not associated with the domain. Embodiments include determining candidate n-grams that are overexpressed in the second plurality of n-grams compared to the third plurality of n-grams. Embodiments include determining a match between a candidate n-gram of the candidate n-grams and a given n-gram of the first plurality of n-grams based on one or more matching factors. Embodiments include amending the first text based on the match between the candidate n-gram and the given n-gram.