Natural Language Search Query Interpretation via Frequency-Based Relevance
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Solution Overview
Problem
Natural language search queries often fail to account for contextually relevant words that are not associated with keywords or genres, leading to incomplete search results, as existing systems ignore words without specific identifiers.
Innovation Solution
A system that processes natural language search queries by identifying and associating each term with its part of speech, determining frequency of occurrence, and marking relevant terms as keywords, using training data to enhance interpretation, and employing machine learning techniques like neural networks to generate vectors for context-based interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based filtering is used to process search queries, then processing speed is maintained, but search accuracy deteriorates because contextually relevant words are ignored
Solution Approach 1:
The system changes the parameter of word relevance assessment from binary (keyword match/no match) to continuous (frequency-based relevance scoring). By calculating frequency of occurrence for each word and comparing against threshold values, the system dynamically determines which words are contextually relevant, thereby improving search accuracy without requiring complex manual tagging.
Solution Approach 2:
The system enables self-service by automatically analyzing and weighting words based on their frequency of occurrence in the dataset, eliminating the need for manual keyword identification or genre association. The processing system autonomously determines relevance through statistical analysis, improving accuracy while keeping processing complexity manageable.
2Measurement precision
If all words in natural language queries are processed equally, then processing simplicity is maintained, but search relevance deteriorates because important contextual words are not distinguished
Solution Approach 1:
The system applies local quality by treating different words in the query differently based on their individual frequency characteristics. Each word receives a customized relevance weight calculated from its frequency of occurrence, rather than applying uniform processing. This allows the system to distinguish between commonly occurring less relevant words and rare but highly relevant words.
3Measurement precision
If frequency-based relevance determination is implemented, then contextual accuracy is improved, but computational requirements increase
Solution Approach 1:
The system implements partial action by applying frequency-based analysis selectively rather than to all possible query components. Words are processed to determine relevance only when needed for search query interpretation, and the analysis is limited to frequency comparison against predefined thresholds, reducing unnecessary computational overhead while maintaining contextual accuracy.
Data Source
AI summary
Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term is associated with a respective part of speech, and a frequency of occurrence of each term in content metadata is determined. A relevance of each term is then determined based on its respective part of speech and frequency. The natural language search query is then interpreted based on the importance or relevance of each term.