Interactive Dictionary Expansion via Centroid Similarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Dictionaries are limited to the words they include at the time of their creation, necessitating an interactive expansion mechanism that allows for user-centric updates based on relevance and similarity analysis.
Innovation Solution
A computer-implemented method for user-centric interactive dictionary expansion, which involves acquiring user objects, calculating a centroid, determining similarity scores, presenting candidate objects, and updating feature weights to maximize similarity scores for inclusion in the dictionary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a dictionary is expanded interactively based on user feedback and similarity analysis, then the dictionary's adaptability and relevance to user needs are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system implements feedback by presenting candidate objects to the user and acquiring user feedback to determine preferred candidates. This feedback loop enables the dictionary to adapt to user needs dynamically, resolving the contradiction between adaptability and complexity by using targeted user interaction to guide expansion rather than unrestricted automated growth
Solution Approach 2:
The system changes parameters by updating feature weights based on user feedback. When a preferred candidate is identified, the system adjusts the weights of object features to maximize similarity scores for that candidate. This parameter adjustment allows the dictionary to evolve its semantic structure adaptively, improving relevance while managing complexity through data-driven optimization
2Manufacturing precision
If multiple candidate objects are presented and user feedback is collected for dictionary expansion, then the quality and relevance of added terms are improved, but the time required for dictionary expansion increases
Solution Approach 1:
The system applies partial action by presenting only a subset of candidate objects to the user rather than all possible candidates. This selective presentation maintains precision by focusing on the most relevant candidates while reducing the time required for user interaction and feedback collection
Solution Approach 2:
The system performs preliminary action by pre-calculating similarity scores and ranking candidates before presenting them to the user. This preliminary processing filters and organizes potential additions in advance, so that when user feedback is collected, the system already has structured information about candidate priorities, reducing overall expansion time
3Stability of the object's composition
If feature weights are updated to maximize similarity scores, then the dictionary's internal consistency and contextual appropriateness are improved, but the computational complexity increases
Solution Approach 1:
The system implements self-service by automatically updating feature weights based on user feedback without requiring manual reconfiguration. The system serves itself by using the feedback information to adjust its internal parameters, maintaining consistency while managing computational complexity through automated optimization
Solution Approach 2:
The system changes parameters by updating feature weights to maximize similarity scores for preferred candidates. This parameter adjustment maintains the dictionary's internal consistency and contextual appropriateness by ensuring that new terms are integrated in a way that preserves semantic relationships, while the optimization process manages computational complexity through mathematical formulation
Data Source
AI summary
A method is provided for dictionary expansion. The method acquires an object from a user and adds the object to a set of objects previously acquired from the user that form an expandable dictionary. The method calculates a centroid based on the set. The method calculates a similarity score of each of a plurality of objects relative to the centroid for each of a plurality of object features to calculate a weighted sum of similarity scores for each of the plurality of objects. The method presents candidate objects selected among the plurality of objects based on the weighted sum. The method acquires, from the user, a preferred candidate object among the candidate objects. The method updates weights of the plurality of features to maximize the weighed sum of similarity scores for the preferred candidate object. The method expands the dictionary by adding the preferred candidate object to the expandable dictionary.