DeepQA Answer Customization via Sentiment Weighting
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Solution Overview
Problem
Current deep question-and-answer systems, despite advancements in natural language processing and semantic search, fail to adequately consider user preferences beyond factual data, leading to suboptimal results in forward-looking decision-making scenarios where personal or subjective preferences are involved.
Innovation Solution
Enhancing search engines to incorporate user preferences through a weighted scoring system that utilizes sentiment analysis on natural language inputs, allowing for the customization of answer outputs based on user-specific criteria, thereby tailoring results to individual user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep question-and-answer systems use traditional semantic search methods, then factual accuracy is improved, but user preference alignment deteriorates
Solution Approach 1:
The system segments the answer selection process into two independent stages: first evaluating factual accuracy through traditional semantic search, then evaluating user preference alignment through sentiment analysis. This allows both factual accuracy and user preference alignment to be optimized separately and combined in the final answer selection.
Solution Approach 2:
The system introduces sentiment analysis as an intermediary component that bridges the gap between traditional semantic search results and user preferences. This intermediary layer analyzes the sentiment of candidate answers and compares it with the user's sentiment profile to determine preference alignment without compromising factual accuracy evaluation.
2Measurement precision
If search systems prioritize factual data relevance, then answer precision is improved, but handling of subjective queries deteriorates
Solution Approach 1:
The system dynamically adjusts the evaluation criteria based on query type. For factual queries, traditional semantic search dominates the evaluation. For subjective queries, sentiment analysis weight is increased to better align with user preferences. This dynamic adjustment allows the system to handle both objective and subjective queries effectively.
Solution Approach 2:
The system changes the evaluation parameters by introducing sentiment analysis scores as a new dimension for answer evaluation. Instead of relying solely on semantic relevance scores, the system now considers both semantic similarity and sentiment alignment, changing the parameter space in which answers are evaluated and ranked.
3Device complexity
If traditional search methods are used, then system simplicity is maintained, but answer customization deteriorates
Solution Approach 1:
The system achieves multi-functionality by making the sentiment analysis component optional and configurable. The same framework can operate in simple mode using only traditional semantic search or in enhanced mode with sentiment analysis enabled. This universality allows the system to provide answer customization when needed while maintaining simplicity when not required.
Solution Approach 2:
The system performs preliminary sentiment analysis on user queries to determine whether customization is needed before proceeding with the full answer generation process. This preliminary action allows the system to activate the more complex sentiment analysis pipeline only when necessary, maintaining simplicity for straightforward queries while enabling customization when required.
Data Source
AI summary
A search engine is enhanced to employ user preferences to customize answer output by, receiving a first plurality of candidate answers and weights to a first search query for a first user from a first stage of a non-semantic search engine routing and processing path; augmenting the weights for the plurality of candidate answers according to a set of preference coefficients according to a first set of preferences for the first user; selecting one or more of the first plurality of candidate answers according to the augmented weights; and producing an output including the selected one or more candidate answers.