Summarized Logical Forms for Search Precision
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Solution Overview
Problem
Conventional question-answering systems face challenges in providing accurate responses due to irrelevant or misleading content in question-answer pairs, leading to decreased user confidence and search precision, as they often index entire documents rather than focusing on informative parts.
Innovation Solution
The method generates summarized logical forms (SLFs) from text answers using techniques like abstract meaning representations, discourse analysis, and generalization, which are then used to index and match with natural language queries, ensuring that only the most informative parts of the answer are indexed and retrieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire document is indexed for question-answering, then the system can retrieve answers, but the precision and user confidence decrease due to irrelevant or misleading content
Solution Approach 1:
The patent segments the answer text into multiple elementary discourse units (EDUs) and further identifies nucleus-satellite relationships within each EDU. This segmentation allows the system to separate informative content (nucleus) from supporting or less important content (satellite), thereby improving search precision by indexing only the most relevant parts rather than the entire document.
Solution Approach 2:
The patent extracts the nucleus portion from each elementary discourse unit, which contains the most informative and relevant content. By taking out only the nucleus parts for indexing and matching with queries, the system eliminates irrelevant or misleading content while preserving the essential information needed for accurate answer retrieval.
2Measurement precision
If comprehensive text is indexed to ensure all possible answers are found, then recall improves, but precision decreases due to irrelevant content
Solution Approach 1:
The patent applies segmentation by dividing the text into elementary discourse units and further segmenting each EDU into nucleus and satellite parts. This hierarchical segmentation creates a structured index that is more manageable and efficient than indexing entire documents, reducing indexing complexity while maintaining answer accuracy.
Solution Approach 2:
The patent applies local quality by treating different parts of the text differently - the nucleus parts are indexed for matching with queries, while satellite parts are processed differently or not indexed at the same level. This differentiated approach improves answer accuracy by focusing on informative content while reducing the overall indexing complexity.
3Loss of time
If the system indexes and processes entire answer texts, then all information is available, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the nucleus portions from elementary discourse units for indexing and query matching. By removing satellite content from the indexing process, the system significantly reduces the volume of data that needs to be processed and stored, thereby decreasing processing time and computational resource requirements while still maintaining access to all necessary information.
Solution Approach 2:
The patent segments text into elementary discourse units and identifies nucleus-satellite relationships, creating a hierarchical structure that enables efficient processing. This segmentation allows the system to process only the essential nucleus parts for query matching, reducing the effective data volume and processing time compared to analyzing entire answer texts.
Data Source
AI summary
Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to generate automated answers to a question using summarized logical forms (SLFs). A myriad of techniques may be utilized to generate one or more summarized logical forms for an answer, where the summarized logical form(s) identifies the main entities/informative portions of the answer. Instead of indexing the whole of the answer as in conventional methods, an answer can be indexed using the summarized logical forms. A subsequent query may be converted to logical form to generate a query representation which can be compared to one or more SLFs of a set of answers. If an SLF of an answer matches the query representation, the answer may be provided in response to the question. By indexing the answer with its informative portions, the speed and accuracy of identifying the answer is improved.