In-Document Search Device for Query Vector Table Data
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Solution Overview
Problem
State-of-the-art question answering systems based on text form architecture face instability in searching data in table form, leading to inconsistent search quality.
Innovation Solution
An in-document search method and device that generates sentence data corresponding to table form data, using a server to receive user queries, generate query vectors, extract candidate table data, and provide responses in either table or sentence form, improving search accuracy and immediacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a QA system uses text form architecture for search, then it achieves human-level performance on text data, but it shows unstable search quality when handling table form data
Solution Approach 1:
The system is designed to handle multiple data formats (text and table forms) using a unified query processing architecture. The vector generation module can process both text queries and table data, converting them into comparable vector representations that enable consistent search operations across different data types, thus achieving both reliability and adaptability
2Measurement precision
If the system provides responses in table form to maintain data structure, then data accuracy is preserved, but user understanding and immediacy may be reduced
Solution Approach 1:
The response generation process is segmented into multiple options: the system can provide answers in table form for accuracy, in sentence form for better readability, or in both formats simultaneously. This segmentation allows users to choose the format that best suits their needs, preserving data accuracy while enhancing user comprehension
Solution Approach 2:
The system introduces sentence form as an intermediary representation between the structured table data and the user's understanding. By generating natural language sentences from table data, the system acts as a mediator that translates structured information into more human-friendly formats without losing the underlying data accuracy
Data Source
AI summary
The present invention relates to an in-document search method and device for a query vector, and an object of the present invention is to improve the accuracy of a response by generating sentence data corresponding to data in a table form stored in database. The in-document search method for a query vector includes a step A of receiving a user query from a user terminal, a step B of generating a user query vector for the user query, a step C of extracting candidate table data based on the user query vector in a data storage module, a step D of searching for a response corresponding to the user query vector in the candidate table data, and a step E of providing the response to the user terminal.