Parallel Answer Scoring for Question Retrieval
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Solution Overview
Problem
Current information retrieval systems lack the capability to accurately answer factual questions based on large collections of documents, as they struggle to understand natural language queries and extract relevant information from structured and unstructured data.
Innovation Solution
A dynamic infrastructure and methodology that generates candidate answers by analyzing multiple passages from various data sources, including structured and unstructured data, using parallel processing to score and rank answers for delivery to users across multiple modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information retrieval systems are used to answer factual questions, then the system can retrieve documents, but it cannot accurately extract and generate answers from structured and unstructured data
Solution Approach 1:
The system segments the answer generation process into distinct modules: query analysis, candidate answer generation from multiple data sources, supporting passage retrieval, parallel scoring, and ranking. This segmentation allows each module to specialize in handling different data types (structured, semi-structured, unstructured) while maintaining overall answer accuracy.
Solution Approach 2:
The system creates a universal answer generation framework that can process multiple data source types (databases, web pages, documents, multimedia) through a unified architecture. The parallel processing capability handles diverse data formats simultaneously, making the system adaptable to various data sources while maintaining consistent answer quality.
2Measurement precision
If the system analyzes multiple passages from various data sources to generate candidate answers, then answer accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary query analysis to identify answer type, constraints, and key terms before generating candidate answers. This preliminary action filters the search space and guides subsequent passage retrieval, reducing the number of passages that need full analysis while maintaining answer accuracy.
Solution Approach 2:
The parallel processing architecture enables continuous evaluation of multiple candidate answers simultaneously rather than sequentially. Multiple scoring modules evaluate different candidate answers from different data sources at the same time, maintaining high processing efficiency while analyzing multiple passages.
3Productivity
If parallel processing is used to score and rank candidate answers, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The parallel processing system is segmented into independent scoring modules, each responsible for evaluating specific aspects of candidate answers (e.g., relevance, accuracy, source reliability). This segmentation allows each module to be simple and focused while the overall system achieves high processing efficiency through parallel execution.
Solution Approach 2:
The system introduces an intermediary ranking function that aggregates scores from multiple parallel scoring modules. This intermediary layer simplifies the complexity by providing a unified interface that combines results from parallel processes, making the system architecture more manageable while maintaining processing efficiency.
4Loss of information
If the system leverages both searchable and non-searchable components in the data corpus, then information completeness improves, but processing difficulty increases
Solution Approach 1:
The system creates a universal processing framework that handles both searchable (structured) and non-searchable (unstructured) data components through the same architecture. The framework uses multiple data sources including databases, web pages, documents, and multimedia, processing them uniformly to ensure complete information retrieval without increasing processing difficulty.
Solution Approach 2:
The query analysis module automatically identifies the types of data sources needed based on the question characteristics, and the system self-configures the appropriate processing pipelines. This self-service capability reduces processing difficulty by eliminating manual configuration for different data types while maintaining information completeness.
Data Source
AI summary
A system, method and computer program product for providing answers to questions based on any corpus of data. The method facilitates generating a number of candidate passages from the corpus that answer an input query, and finds the correct resulting answer by collecting supporting evidence from the multiple passages. By analyzing all retrieved passages and that passage's metadata in parallel, there is generated an output plurality of data structures including candidate answers based upon the analyzing. Then, by each of a plurality of parallel operating modules, supporting passage retrieval operations are performed upon the set of candidate answers, and for each candidate answer, the data corpus is traversed to find those passages having candidate answer in addition to query terms. All candidate answers are automatically scored causing the supporting passages by a plurality of scoring modules, each producing a module score. The modules scores are processed to determine one or more query answers; and, a query response is generated for delivery to a user based on the one or more query answers.