Semantic Completeness Assessment for Domain-Specific Q&A Systems
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Solution Overview
Problem
Current question-and-answer systems fail to assess the semantic completeness of documents, particularly in specific domains like financial operations, leading to incorrect answers as they do not distinguish between contextually relevant and irrelevant information.
Innovation Solution
A method using a cogency module with a feedforward neural network that evaluates the semantic completeness of documents by applying domain-specific quality control questions, incorporating metadata features to verify the validity of answers and build a domain-optimized corpus for accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deep learning question-and-answer system uses general documents as reference, then it can process a wide range of queries, but it cannot ensure domain-specific semantic completeness and accuracy
Solution Approach 1:
The patent applies local quality by making the reference corpus domain-specific rather than general. The system selectively curates documents from particular domains (e.g., financial operations) to create specialized reference materials. This allows the system to maintain high reliability within specific domains while preserving broad adaptability through the ability to select and switch between different domain-specific corpora based on query requirements.
2Productivity
If the system uses traditional NLP methods to analyze documents, then it can process information efficiently, but it cannot distinguish between contextually relevant and irrelevant information in specific domains
Solution Approach 1:
The patent applies preliminary action by pre-processing and curating domain-specific reference corpora before they are used for question answering. The system performs advance filtering, selection, and organization of documents based on domain relevance, creating optimized reference materials that contain only contextually appropriate information. This preliminary curation enables the system to maintain high processing speeds while ensuring contextual precision, as the filtering work is done in advance rather than during query processing.
3Adaptability or versatility
If the system trains with large amounts of general training data, then it can learn broad patterns, but it cannot achieve high accuracy with minimal domain-specific training data
Solution Approach 1:
The patent applies the extraction principle by isolating and separating domain-specific information from general training data. The system extracts only the necessary domain-specific features, terminology, and contextual patterns needed for accurate domain-specific answering. This allows the model to retain broad pattern recognition capabilities from general training while achieving high domain-specific accuracy through targeted extraction of relevant features, reducing the need for extensive domain-specific training data.
Data Source
AI summary
A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.