Context-Specific Term Generation via Frequency Threshold Filtering
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Solution Overview
Problem
Software programs and individuals face difficulties in interpreting new terms used in specific contexts, such as within educational courses or organizational groups, as these terms may not be recognized by standard language dictionaries.
Innovation Solution
A method for generating context-specific terms by obtaining terms from electronic files associated with a given context, comparing them with expected terms, determining relevance, and adding valid terms to a collection if their frequency meets a threshold, which can be used in spell-check applications and for seeding glossaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard language dictionaries are used for term interpretation, then general language understanding is maintained, but context-specific terms cannot be recognized or interpreted correctly
Solution Approach 1:
The system segments the vocabulary into standard terms and context-specific terms by comparing terms from electronic files against expected terms from standard dictionaries. This segmentation allows the system to handle general language understanding separately from specialized term recognition, resolving the contradiction between maintaining standard language comprehension and recognizing context-specific terminology.
Solution Approach 2:
The system introduces an intermediary mechanism (term frequency analysis and relevance determination) between the standard dictionary and context-specific terms. By using frequency thresholds and relevance scoring, the system mediates between general language expectations and specialized term recognition, allowing context-specific terms to be identified and managed without losing their specialized meanings.
2Reliability
If all candidate terms are added to context-specific terms collection, then comprehensive coverage of specialized vocabulary is achieved, but irrelevant or invalid terms are included
Solution Approach 1:
The system applies partial action by using frequency thresholds to filter candidate terms. Instead of adding all candidate terms, it adds only those that meet the minimum frequency threshold, thereby achieving reasonable coverage of context-specific terms while filtering out irrelevant or invalid terms. This partial approach balances reliability and productivity.
Solution Approach 2:
The system implements feedback through relevance determination and validation steps that assess whether candidate terms should be added to the context-specific terms collection. This feedback mechanism ensures that only valid and relevant terms are included, maintaining reliability while preventing the inclusion of spurious terms that would waste processing resources.
3Measurement precision
If manual review of all candidate terms is performed, then high accuracy in term selection is achieved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary automated filtering of candidate terms using frequency thresholds and relevance determination before any manual review. This preliminary action pre-processes the candidate terms, reducing the number of terms that require manual verification and thereby maintaining accuracy while significantly reducing time consumption.
Solution Approach 2:
The system enables self-service through automated term frequency analysis and relevance scoring that can independently identify and validate context-specific terms without requiring manual intervention for every term. This self-service capability maintains high accuracy through automated validation while eliminating time-consuming manual review of each candidate term.
Data Source
AI summary
Various embodiments are described herein that generally relate to systems and methods for generating context specific terms and performing various actions based on the context specific terms. One example embodiment includes a computer-implemented method for generating context specific terms comprising obtaining a collection of terms from at least one electronic file associated with a given context; comparing the collection of terms with a collection of expected terms to generate candidate terms that are not in the collection of expected terms; determining a relevance for each of the candidate terms; and determining whether to add a given candidate term to a collection of context specific terms for the given context if the relevance for the given candidate term is above a threshold.