Query Augmentation and Semantic Ranking for Response Retrieval
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Solution Overview
Problem
Existing keyword-based retrieval solutions for automated customer support systems fail to effectively capture semantic similarities and relationships between user queries and responses, leading to low recall and inability to determine the best answer, due to their insensitivity to syntactic variants and lack of feedback loops.
Innovation Solution
A system utilizing machine learning models for query augmentation and semantic comparison, which generates semantically similar variations of user queries, performs TF-IDF processing, and uses IR and NLP algorithms to rank response candidates based on semantic similarity, providing a final response candidate that balances syntactic and semantic relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If keyword-based retrieval solutions are used, then the system is simple and fast, but the recall and ability to capture semantic similarities is low
Solution Approach 1:
The system segments the retrieval process into multiple stages: initial keyword-based filtering to identify candidate responses, followed by semantic similarity analysis using machine learning models to rank and select the best match. This segmentation allows the system to maintain simplicity in the first stage while adding sophisticated semantic analysis only where needed.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the simple keyword retrieval and the final response selection. These models act as mediators that compute semantic similarities and relationships, bridging the gap between basic keyword matching and advanced semantic understanding without completely replacing the simple retrieval mechanism.
2Adaptability or versatility
If keyword-based retrieval is used, then the system operates quickly, but it is insensitive to syntactic variants of the same user query
Solution Approach 1:
The system performs preliminary keyword-based retrieval to quickly identify a subset of candidate responses that match the user query. This preliminary action filters out clearly irrelevant responses before applying more time-consuming semantic analysis, thus maintaining efficiency while improving adaptability to syntactic variants.
Solution Approach 2:
Instead of applying full semantic analysis to all possible responses, the system applies it only to a partial subset of candidates identified by keyword matching. This partial action approach maintains processing speed while still capturing syntactic variants that the keyword search initially identified as relevant.
3Measurement precision
If keyword-based retrieval solutions are used, then the system is easy to implement, but it cannot capture the relationship between queries and answers
Solution Approach 1:
The system implements feedback mechanisms where the results of semantic similarity analysis are fed back to refine and re-rank the candidate responses. This feedback loop allows the system to continuously improve the accuracy of query-answer relationship measurement by using the insights from semantic analysis to adjust the initial keyword-based rankings.
Solution Approach 2:
The patent substitutes the purely mechanical keyword-matching mechanism with a hybrid approach that incorporates machine learning-based semantic analysis. This substitution replaces the insufficient keyword-based relationship measurement with a more sophisticated model that understands semantic relationships, while retaining the mechanical keyword filtering as a preliminary step.
Data Source
AI summary
Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.