Syntactic Dependency Synonym Suggestions for Mobile Text Input
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Solution Overview
Problem
Existing software-based thesaurus tools integrated with graphical, touch-based keyboards or mobile computing devices are not easily accessible during text input processes and often return irrelevant synonyms due to their inability to consider the context surrounding a target word.
Innovation Solution
The solution involves determining a target word in a text sequence, extracting syntactic dependency data, and inputting this data into a machine learning model trained on syntactic dependency data to produce a set of candidate synonyms, which are then provided to the graphical user interface for automatic suggestion during text input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing thesaurus tools are integrated with graphical keyboards or mobile devices, then accessibility during text input is improved, but the tools return irrelevant synonyms due to inability to consider context
Solution Approach 1:
The system uses syntactic dependency data as feedback to continuously refine and filter synonym suggestions. By analyzing the grammatical relationships and contextual structure of the input text, the system adjusts which synonyms are presented to the user, ensuring they are contextually relevant rather than merely lexically similar.
Solution Approach 2:
The invention changes the parameters used for synonym selection from simple lexical matching to complex syntactic dependency analysis. By incorporating part-of-speech tags, dependency relationships, and contextual grammatical structure as selection parameters, the system transforms the quality of synonym suggestions while maintaining ease of access during text input.
2Device complexity
If thesaurus tools require explicit user selection from menu bars or special input boxes, then processing complexity is reduced, but user productivity and ease of use deteriorate
Solution Approach 1:
The system implements self-service by automatically detecting when the user wants synonym suggestions through cursor positioning or text selection, without requiring explicit menu selections or activation commands. The thesaurus function activates itself based on implicit user intent, maintaining simplicity while improving productivity.
Solution Approach 2:
The system performs preliminary analysis of the text context and syntactic dependencies before the user even requests synonyms. By pre-processing the textual and grammatical context, the system is ready to provide relevant suggestions immediately when triggered, reducing the perceived delay and effort for the user.
3Productivity
If automatic synonym suggestion is implemented during text input, then user productivity is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by providing synonym suggestions for only the most relevant words or contexts rather than analyzing every word in the text. By selectively applying syntactic dependency analysis to key terms and using pre-computed linguistic data, the system reduces processing time while maintaining suggestion quality.
Solution Approach 2:
Linguistic data such as part-of-speech tags and dependency relationships are pre-computed and stored during text input, so that when synonym suggestions are needed, the system can quickly retrieve and filter from pre-analyzed data rather than performing full syntactic analysis in real-time.
Data Source
AI summary
In an embodiment, the disclosed technologies are capable of identifying a target word within a text sequence; displaying a subset of candidate synonyms for the target word, determining a synonym selected from the subset of candidate synonyms, and replacing the target word with the selected synonym, where the subset of candidate synonyms has been created using syntactic dependency data for the target word.