NLP Response Optimizer Using Social Network Analysis
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Solution Overview
Problem
Current Question Answering (QA) systems using Natural Language Processing (NLP) face challenges in determining the confidence level of answers, often resulting in low confidence due to contradictory information or reliance on less relevant sources, which affects the quality and accuracy of responses.
Innovation Solution
The integration of Social Network Analysis (SNA) metrics such as degree centrality, betweenness centrality, closeness centrality, Eigenvalue, hub, and authority to weight information and enhance the confidence level of answers by identifying and prioritizing expert opinions within social networks, thereby improving the quality of responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NLP QA systems query data repositories using standard search algorithms, then they can retrieve candidate answers efficiently, but the confidence level of answers remains low due to contradictory information and reliance on less relevant sources
Solution Approach 1:
The patent introduces social network analysis metrics as an intermediary layer between the NLP QA system and the data repositories. This intermediary evaluates the reliability of information sources by analyzing network centrality metrics (degree, betweenness, closeness, Eigenvalue, hub, and authority) of nodes representing sources. The system uses this intermediary assessment to weight and prioritize candidate answers, resolving contradictions by selecting answers from high-centrality, trusted nodes in the social network graph.
2Adaptability or versatility
If the QA system relies on multiple algorithms to return candidate answers following the wisdom of crowds, then it can process diverse information, but it cannot distinguish between relevant expert opinions and less reliable sources
Solution Approach 1:
The patent applies local quality by assigning different reliability weights to different information sources based on their local properties in the social network. Each node (information source) is evaluated using its specific centrality metrics - degree centrality for connectivity, betweenness centrality for information flow control, closeness centrality for proximity to other nodes, Eigenvalue for overall influence, hub for connectivity to authorities, and authority for being cited by others. This local quality assessment allows the system to precisely identify expert opinions while maintaining adaptability to process diverse information from various network positions.
Data Source
AI summary
Methods and a system are provided. A method includes optimizing, by a natural language processing based response optimizer having a processor, responses in an online question and answer session using natural language processing. The optimizing step includes deriving a candidate answer to a question posed in natural language and gathering support for the candidate answer by accessing a social network. The support is weighted by at least one of degree centrality, betweenness centrality, closeness centrality, Eigenvalue, hub, and authority of nodes in the social network that are associated with the support.