Natural Language Question Answering System for Broad Queries
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Solution Overview
Problem
Current natural language processing systems are unable to provide satisfactory responses to broad, non-specific questions that do not relate to specific entities or data records, as they struggle to identify relevant records and aggregate information from different entities.
Innovation Solution
The implementation of a natural language-based question answering system that uses machine learning techniques, such as named entity recognition, relevance filtering, and question-to-answer models, to process natural language inputs, extract relevant content items, and generate responses to general questions by determining relevant attributes and aggregating attribute values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional natural language processing systems are used to process broad, non-specific questions, then the system structure remains simple, but the system cannot identify relevant records and generate satisfactory responses
Solution Approach 1:
The system segments the question answering process into distinct functional modules: a question processing module that analyzes the input question and identifies broad, non-specific questions; a content retrieval module that searches for relevant content items; an attribute determination module that identifies relevant attributes; and an information aggregation module that combines attribute values. This segmentation allows each module to specialize in specific tasks, improving the overall ability to handle broad questions while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system performs preliminary processing of the input question to determine whether it is a broad, non-specific question before proceeding with full question answering. This preliminary classification enables the system to activate specialized processing pathways for broad questions, retrieving and aggregating information from multiple content items based on identified attributes, rather than attempting to answer all questions using a single generic approach.
2Reliability
If the system retrieves and aggregates information from multiple content items, then the ability to answer broad questions improves, but the processing time and computational resources increase
Solution Approach 1:
The system determines a specific number of content items to retrieve based on the broad question, rather than attempting to retrieve all possible relevant content. This controlled retrieval of a predetermined quantity of content items balances the need for sufficient information to answer broad questions with the constraint of processing time and computational resources.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the type of question received. For broad, non-specific questions, the system activates information aggregation across multiple content items with determined attribute values. For specific entity questions, the system uses more direct retrieval methods. This parameter adjustment optimizes processing efficiency while maintaining answer quality for different question types.
Data Source
AI summary
Natural language-based question answering systems and techniques are generally described. In some examples, a natural language processing system, first natural language data. The natural language processing system may determine first slot data included in the first natural language data. A set of content items associated with the first slot data may be determined. A first machine learning model may use the first natural language data to generate prediction data associated with a first attribute among a list of attributes of the set of content items. In some examples, a first value associated with the first attribute for a first content item of the set of content items may be determined. Second natural language data may be generated based at least in part on the first value. The second natural language data may include a response to the first natural language data.