Encoder Decoder Model for Domain Misspelling Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing systems face challenges in interpreting user inputs with domain-specific misspellings, leading to ambiguous queries that hinder effective search and response generation, as they lack the capability to understand the underlying meaning behind user requests.
Innovation Solution
A method utilizing machine learning models, specifically encoder and decoder models, to receive flawed inputs, encode them at a character level to create a context vector, and decode it to produce a rephrased input that corrects domain-specific misspellings, thereby improving input clarity and search accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computing systems process user inputs directly, then processing speed is maintained, but interpretation accuracy deteriorates due to domain-specific misspellings
Solution Approach 1:
The patent introduces an intermediary processing layer between user input and the computing system. This layer includes a classifier that identifies domain-specific misspellings and an encoder-decoder model that corrects them. The intermediary transforms flawed inputs into corrected versions before processing, thereby improving interpretation accuracy without requiring complete system redesign.
Solution Approach 2:
The patent segments the input processing into distinct functional components: a classifier module for detecting misspellings, an encoder module for creating context representations, and a decoder module for generating corrected text. This segmentation allows each component to specialize in a specific task, improving overall accuracy while maintaining manageable system complexity.
2Measurement precision
If encoder-decoder models are used to correct misspellings, then query clarity is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the encoder-decoder model on domain-specific data before deployment. The classifier is also pre-trained to recognize common domain-specific misspellings. This preliminary preparation enables the system to quickly identify and correct errors during actual operation, reducing the time penalty of using complex models.
Solution Approach 2:
The patent changes the parameter of processing depth by using character-level encoding rather than word-level or sentence-level processing. This parameter change allows the model to capture subtle spelling variations more effectively while maintaining reasonable processing speeds, as character-level operations are computationally more efficient than higher-level linguistic unit processing.
3Measurement precision
If domain-specific vocabulary is expanded, then recognition accuracy is improved, but false positive rate increases
Solution Approach 1:
The patent applies local quality by training the classifier and encoder-decoder model specifically on domain-related text and vocabulary. Rather than using general-purpose language models, the system adapts its parameters and knowledge base to the specific domain context. This localized adaptation improves recognition accuracy for domain-specific terms while reducing false positives, as the model learns to distinguish between intentional domain vocabulary and actual misspellings within that specific context.
Data Source
AI summary
The invention relates to a method. The method includes receiving a flawed input comprising a domain specific misspelling. The method further includes encoding, by an encoder machine learning model executing on a computer processor, the flawed input on a per character basis to create a context vector. The method further includes decoding, by a decoder machine learning model executing on the computer processor, the context vector on the per character basis to create a rephrased input lacking the domain specific misspelling. The method further includes presenting the rephrased input.