Information Retrieval System for Instructional Documents
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Solution Overview
Problem
Conventional information retrieval systems for instructional documents, such as e-manuals, face challenges in retrieving relevant information from multiple non-contiguous sections, leading to cumbersome user experiences and increased customer support queries, as they often return irrelevant or incomplete answers due to their inability to handle long-range dependencies and non-contiguous text and image sections.
Innovation Solution
A system and method that utilize pre-trained encoders like BERT and RoBERTa, combined with TF-IDF and sequential classification models, to compare queries with both text and image sections, generate relevant image-text combinations, and provide corrected answers by integrating device context information, enabling efficient retrieval of information from multiple non-contiguous sections within instructional documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional searching techniques are used to search e-manuals, then all occurrences of query terms are returned, but the relevance and accuracy of the answers deteriorates due to inability to distinguish relevant from irrelevant occurrences
Solution Approach 1:
The e-manual is segmented into multiple sections and paragraphs, with each segment independently processed and evaluated. The system divides the document into manageable units (sections, paragraphs) and applies retrieval operations to each segment, allowing precise identification of relevant occurrences while filtering out irrelevant ones through localized processing.
Solution Approach 2:
The system changes the search parameters from simple keyword matching to multi-parameter evaluation including semantic similarity, contextual relevance, and section-level importance. By transforming the search query into enriched representations and evaluating multiple dimensions (textual similarity, contextual fit, section relevance), the system achieves high precision in distinguishing relevant from irrelevant occurrences.
2Loss of information
If e-manuals are structured in sequential fashion with lengthy instructions, then comprehensive information is provided, but the difficulty of retrieving specific information increases due to long-range dependencies and non-contiguous sections
Solution Approach 1:
The sequential e-manual structure is segmented into discrete, indexable sections and paragraphs. Each segment is independently processed and stored with its positional information, enabling the system to quickly locate and retrieve specific segments regardless of their position in the overall sequential document structure.
Solution Approach 2:
An intermediary indexing structure is introduced between the sequential e-manual content and the retrieval query. The indexing system acts as a mediator that maps queries to relevant sections, bridging the gap between the sequential document structure and the need for rapid, accurate information retrieval across non-contiguous sections.
3Adaptability or versatility
If users are provided with multiple pages of keyword matches, then search coverage is improved, but the ease of operation deteriorates as users must manually read multiple pages to find answers
Solution Approach 1:
The system extracts and presents only the most relevant information segments that directly answer the user's query, rather than displaying all keyword matches. By extracting and prioritizing the most relevant sections based on semantic similarity and contextual relevance, the system reduces the amount of information the user must manually review while maintaining comprehensive search coverage.
Solution Approach 2:
The retrieval system incorporates feedback mechanisms that prioritize and rank search results based on relevance scoring. The system continuously refines its retrieval based on query analysis and document characteristics, presenting results in a prioritized order that guides users to the most relevant information first, improving ease of operation while maintaining adaptability.
Data Source
AI summary
A system for retrieving information from an instructional document, includes a processor configured to: receive a query from a user; compare the query with one or more text sections in the instructional document; obtain, from the one or more text sections, top x text sections relevant to the query using a pre-trained encoder; compare the query with one or more images in the instructional document; obtain, from the images, top y images relevant to the query; generate top y image-text sections based on the top y images; obtain top k sections from the top x text sections and the top y image-text sections; obtain one or more most relevant sections from the top k sections using a domain-specific pre-trained encoder and a sequential classification model; and generate an answer based on the one or more most relevant sections and device context information using the domain-specific pre-trained encoder.