Dynamic Domain Dictionary Creation via Multi-Score Word Classification
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Solution Overview
Problem
Existing knowledge bases hold vast amounts of information but are inefficient for finding relevant data due to the cumbersome process of extracting useful insights, necessitating transformation into domain-specific dictionaries for applications like document classification and machine translation.
Innovation Solution
A method and system that compute syntactic, usage, and contextual similarity scores for input words against domain-specific words, determining their belongingness to domains through weighted overall similarity scores, enabling dynamic creation and building of domain dictionaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If knowledge base is transformed into domain-specific dictionaries, then information retrieval efficiency is improved, but the complexity of the transformation process increases
Solution Approach 1:
The knowledge base is segmented into multiple domain-specific dictionaries based on different domains (e.g., medical, legal, technical). Each dictionary contains terms and concepts relevant to its specific domain, allowing efficient retrieval by directing queries to the appropriate domain dictionary rather than searching the entire knowledge base.
Solution Approach 2:
The system performs preliminary classification of terms and concepts into domain-specific categories during the transformation phase. By pre-organizing the knowledge base structure into domain-specific dictionaries with predefined categories and relationships, the system eliminates the need for complex real-time classification during information retrieval operations.
2Measurement precision
If multiple similarity scores are computed for word classification, then classification accuracy is improved, but computational time increases
Solution Approach 1:
The system computes multiple similarity scores (syntactic, usage, and contextual) for each word against domain-specific terms, which is more than a single similarity metric would provide. This excessive computation in the classification phase ensures high accuracy by considering multiple dimensions of word meaning and relationship, with the trade-off being acceptable due to the one-time nature of dictionary construction.
Solution Approach 2:
The system changes the parameters of similarity measurement by using three different types of similarity scores instead of one. Each score type measures a different aspect of word relationship (syntactic structure, usage patterns, contextual meaning), and the combination of these parameter changes provides comprehensive classification accuracy.
3Adaptability or versatility
If domain dictionary is dynamically created and built, then adaptability to new domains is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic creation and building of domain dictionaries, allowing new domains to be added and existing domains to be updated based on incoming data. The dictionary structure and content are not fixed but can evolve over time, enabling the system to adapt to new domains and terminology without requiring complete system redesign.
Solution Approach 2:
The system performs self-service by automatically computing similarity scores, classifying words, and organizing them into appropriate domain dictionaries without requiring manual curation for each new domain. The automated classification mechanism serves the system itself, reducing the complexity burden that would otherwise require significant human intervention.
Data Source
AI summary
This disclosure relates to system and method for dynamically creating and building a domain dictionary. In one embodiment, the method comprises computing a syntactic similarity score, a usage similarity score, and a contextual similarity score for an input word with respect to each of a plurality of domain specific words in each of a plurality of existing domains. The method further comprises computing a weighted overall similarity score for the input word with respect to each of the plurality of domain specific words in each of the plurality of existing domains based on the syntactic similarity score, the usage similarity score, and the contextual similarity score. The method further comprises determining belongingness of the input word to each of the plurality of existing domains based on the weighted overall similarity score.