Question Answering Using Semantic Distance Thresholds
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Solution Overview
Problem
Conventional question answering systems lack the ability to effectively utilize semantic distances between subjects to provide accurate and relevant information, limiting their ability to answer questions by relying solely on manual classification and structured data.
Innovation Solution
A method that employs latent Dirichlet allocation (LDA) to generate topic vectors for subjects, calculates semantic distances between them using dot products, and utilizes a threshold value to determine the relevance of additional information, allowing for the modification of questions to locate related data and improve search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional document search technology is used to return documents based on keyword queries, then the system is simple to implement, but the system cannot provide precise answers to natural language questions and lacks semantic understanding
Solution Approach 1:
The patent segments the question answering process into multiple independent modules: hypothesis generation, evidence gathering, data analysis, and answer scoring. Each module performs a specific function, allowing the complex QA task to be broken down into manageable components that can be processed separately and then integrated to produce precise answers.
Solution Approach 2:
The patent introduces intermediate representations including topic vectors, semantic distance calculations, and confidence scores as mediators between the input question and the final answer. These intermediaries enable the system to bridge the gap between unstructured natural language queries and structured answer generation, improving precision while managing complexity through standardized intermediate formats.
2Adaptability or versatility
If manual classification and structured data are used alone, then the system is easier to manage, but the system cannot effectively process unstructured natural language and lacks semantic distance calculation capability
Solution Approach 1:
The patent replaces manual classification mechanisms with automated topic vector generation using latent Dirichlet allocation (LDA). This substitution enables the system to automatically process unstructured natural language data without requiring manual categorization, significantly improving adaptability to diverse query types while managing complexity through algorithmic automation.
Solution Approach 2:
The patent transforms static structured data into dynamic semantic representations by calculating semantic distances between topic vectors. This parameter transformation allows the system to adapt to various natural language queries by measuring semantic proximity rather than relying on fixed categorical classifications, enhancing versatility while using mathematical parameters to manage the complexity of unstructured data processing.
3Reliability
If semantic distance calculation using topic vectors is implemented, then the system can provide more relevant information, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating topic vectors for all subjects and storing them in a database before actual question processing. This pre-computation allows the system to quickly retrieve and compare topic vectors during query processing without performing complex calculations in real-time, thereby maintaining high information relevance while reducing processing time for actual queries.
Solution Approach 2:
The patent implements partial action by calculating semantic distances only for topic vectors that are potentially relevant to the query, rather than computing all possible distances. The system uses threshold-based filtering to limit calculations to a subset of candidate subjects, maintaining reliable information retrieval while significantly reducing the computational burden and processing time required for semantic distance calculations.
Data Source
AI summary
A technique for answering questions includes receiving a question directed to a first subject. A mathematical operation is performed between each of one or more first topic vectors (associated with the first subject) and each of one or more second topic vectors (associated with a second subject) to generate respective strength values. Relevant ones of the respective strength values are summed to provide an overall strength value, which is utilized to determine a semantic distance (SD) between the first subject and the second subject. In response to the SD being within a threshold distance value (TDV), information associated with the first subject and the second subject is utilized to answer the question. In response to the SD not being within the TDV, information associated with the first subject is utilized to answer the question.