Word Order Suggestion Model Using Document Formatting and Metadata
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Solution Overview
Problem
Conventional spell check systems fail to provide contextually relevant word suggestions for misspelled words due to their reliance on similarity-based dictionary matching, leading to irrelevant and erroneous suggestions, which increases user frustration and resource consumption.
Innovation Solution
A word order suggestion model that analyzes the unformatted content, formatting, and metadata of an electronic document to determine the context and relevance of word suggestions, prioritizing suggestions based on frequency, formatting importance, and file metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spell check systems use similarity-based dictionary matching to provide word suggestions, then the system is simple and fast to implement, but the suggestions are irrelevant and erroneous due to lack of context awareness
Solution Approach 1:
The system performs preliminary analysis of the document's formatting, metadata, and content structure before generating word suggestions. This advance preparation allows the system to understand the context in which the misspelled word appears, enabling it to provide more accurate and relevant suggestions while maintaining computational efficiency through pre-computed document characteristics.
Solution Approach 2:
The system applies different analysis methods to different parts of the document. Specifically, it analyzes formatting properties (such as bold, italic, underline), metadata (such as document type, author information), and surrounding content locally around the misspelled word. This localized contextual analysis improves suggestion accuracy without requiring complex global document processing.
2Loss of time
If conventional spell check systems provide word suggestions without contextual analysis, then the processing is fast and resource-efficient, but the suggestions are irrelevant requiring users to spend extra time identifying correct spellings
Solution Approach 1:
The system pre-analyzes document formatting, metadata, and content structure before the user encounters the misspelled word. This preliminary contextual analysis is stored and readily available when a spelling check is requested, enabling the system to provide accurate suggestions immediately without requiring time-consuming analysis at the moment of correction.
Solution Approach 2:
The system automatically analyzes and prioritizes word suggestions based on the document's contextual information without requiring user intervention. It self-adjusts the suggestion ranking by evaluating formatting importance, metadata relevance, and surrounding content, thereby reducing the time users need to spend manually identifying correct spellings while maintaining high processing efficiency.
3Adaptability or versatility
If conventional spell check systems rely on basic dictionary matching, then the system complexity is low, but the suggestions fail to account for document context leading to erroneous recommendations
Solution Approach 1:
The system enhances context awareness by analyzing specific local properties of the document: formatting attributes (bold, italic, underline, color), metadata (document type, author, creation date), and surrounding text content. Each of these local analyses contributes to understanding the context of the misspelled word, enabling the system to provide adaptable suggestions tailored to the specific document without requiring a completely complex new architecture.
Solution Approach 2:
The contextual analysis is divided into separate, manageable segments: formatting analysis, metadata analysis, and content analysis. Each segment processes a specific aspect of the document independently and contributes its findings to the overall suggestion ranking. This segmentation allows the system to achieve high adaptability through multiple analysis dimensions while keeping the overall system complexity manageable through modular design.
Data Source
AI summary
Non-limiting examples of the present disclosure describe processing that improves suggestions for a misspelt word. Examples described herein relate to processing executed by a client device or computing devices connected via a distributed network. In one example, an electronic document that comprises a misspelt word is accessed. An order of word suggestions for the misspelt word is determined based on an application of an exemplary word order suggestion model that analyzes the misspelt word based on: an analysis of an unformatted state of content within the electronic document and an analysis of formatting associated with content of the electronic document. Further aspects of an electronic document including file metadata may be analyzed by the word order suggestion model. A listing of suggestions for the misspelt word is provided, where an order of word suggestions included in the listing is determined based on the application of the word order suggestion model.