Summarized Logical Forms for Question Answering
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Solution Overview
Problem
Current question answering systems face challenges in providing exact and timely answers due to reliance on statistical methods, leading to irrelevant or misleading responses, especially in domains requiring high-quality content delivery, such as product usability and service recommendations.
Innovation Solution
The use of summarized logical forms (SLFs) generated through semantic and discourse parsing, abstract meaning representations, and generalization techniques to index the most informative parts of answer texts, enabling precise matching with natural language queries and improving search precision and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If statistical methods are used for question answering, then the system can process queries quickly, but the accuracy and relevance of answers deteriorate
Solution Approach 1:
The patent introduces summarized logical forms as an intermediary representation between natural language queries and answer texts. These SLFs capture the essential semantic structure of answers in a standardized format, enabling precise matching with parsed queries while maintaining processing efficiency. The SLF acts as a mediator that bridges the gap between statistical processing and semantic accuracy.
2Loss of information
If comprehensive answer texts are indexed, then more information is available for matching, but the complexity of the indexing system increases
Solution Approach 1:
The patent extracts only the essential informative parts of answer texts and represents them as summarized logical forms. Instead of indexing entire answer texts, the system extracts key semantic elements and relationships, storing them in a compact SLF format. This extraction approach retains the critical information needed for accurate matching while significantly reducing indexing complexity.
Solution Approach 2:
The patent segments answer texts into distinct logical components represented as SLFs. Each SLF captures a specific aspect or proposition from the answer text, allowing the system to index and match individual semantic units rather than processing entire texts. This segmentation enables efficient retrieval while preserving essential information.
3Measurement precision
If semantic parsing is applied to all queries, then matching precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary semantic parsing to generate summarized logical forms for answer texts during an offline preprocessing stage. By pre-computing the SLFs for all candidate answers, the system avoids the need to perform complex semantic parsing for every query at runtime. This preliminary action shifts the computational burden to an offline phase, enabling fast online matching while maintaining high precision.
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 manually or automatically 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 matched to the SLF and 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.