Natural Language Query Interpretation Using Frequency-Based Keywords
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Solution Overview
Problem
Natural language search queries often fail to account for contextual relevance of words that are not associated with any keyword or genre, leading to incomplete or irrelevant search results.
Innovation Solution
Systems and methods that interpret natural language search queries by identifying terms with contextual relevance, associating them with parts of speech, determining frequency of occurrence, and using training data to identify keywords, and employing machine learning techniques like neural networks to enhance search query interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simple filtering is applied to process natural language search queries, then processing speed is improved, but search accuracy deteriorates because contextual relevance of words is ignored
Solution Approach 1:
The system changes the parameter of word relevance assessment from binary (keyword match/no match) to a spectrum based on frequency of occurrence. Words are evaluated based on their frequency in content metadata, allowing rare but contextually important words to be identified as relevant keywords while common stop words are filtered out. This resolves the contradiction by maintaining processing efficiency while improving search accuracy through a more nuanced relevance parameter.
Solution Approach 2:
The system performs preliminary analysis by determining the frequency of occurrence for each word in the query against a database of content metadata before executing the search. This preliminary action identifies which words should be treated as keywords versus stop words, allowing the subsequent search filtering to be both fast and accurate. The frequency analysis is done in advance, enabling the system to resolve the speed-accuracy contradiction.
2Reliability
If all words in the query are processed as potential keywords, then search completeness is improved, but processing complexity increases
Solution Approach 1:
The system introduces a frequency-based parameter to automatically differentiate between keywords and stop words. By calculating the frequency of each word's occurrence in content metadata and comparing it against a threshold, the system automatically determines which words require processing. This parameter-based approach maintains search completeness for relevant words while reducing processing complexity by filtering out common words that don't contribute to search specificity.
Solution Approach 2:
The system enables queries to self-determine their own keyword structure through automatic frequency analysis. Each word in the query is independently evaluated based on its frequency characteristics, allowing the system to automatically identify which words should be processed without requiring manual keyword specification or complex parsing rules. This self-service approach balances completeness and complexity.
3Measurement precision
If frequency threshold is set low to capture more relevant terms, then search accuracy is improved, but false positives increase
Solution Approach 1:
The system dynamically adjusts the frequency threshold parameter based on the specific query and content domain. Rather than using a fixed low threshold that would capture too many words, the system calculates thresholds adaptively by analyzing the distribution of word frequencies in the content metadata. This allows the system to maintain high search accuracy while minimizing false positives by setting the threshold at the optimal point in the frequency distribution.
Solution Approach 2:
The system uses feedback from the frequency analysis results to refine keyword identification. By examining which words at a given frequency threshold are actually relevant versus false positives, the system can adjust the threshold parameter iteratively. This feedback mechanism allows the system to optimize the balance between capturing relevant terms and avoiding false positives, improving both accuracy and reliability.
Data Source
AI summary
A frequency of occurrence for each term in a training data set is determined in relation to the entire training data set. A relational data structure is generated that associates each term in the training data with its respective frequency. Any term that has a frequency below a threshold frequency is then added to a list of relevant words. When a natural language search query is received, a plurality of terms in the natural language search query are identified and compared with the list of relevant words. If any term of the natural language search query is included in the relevant words list, that term is identified as a keyword. The natural language search query is then interpreted based on any identified keywords.