Document Retrieval Explainability for Query-Content Mismatch
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Solution Overview
Problem
Existing knowledge retrieval systems struggle with inefficiencies in document comprehension due to a disconnect between user queries and available content, often failing to account for the user's unfamiliarity with specific terms or omitted concepts, leading to rigid and inadequate document retrieval.
Innovation Solution
A computing system that utilizes neural networks and NLP techniques to generate feature vectors for documents and queries, calculating similarity and explainability scores to refine document retrieval by allowing users to interactively select and visualize relevant words, enhancing document comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language queries are used for document retrieval, then ease of operation is improved, but measurement precision deteriorates due to disconnect between user queries and available content
Solution Approach 1:
The patent introduces query expansion terms and alternative phrasings as intermediaries between the user's natural language query and the document content. These intermediary terms bridge the semantic gap by incorporating domain-specific vocabulary and related concepts that may not be explicitly mentioned in the user's query but are present in the target documents.
Solution Approach 2:
The system dynamically adjusts query parameters by generating multiple versions of the query with different term combinations, weights, and expansions. This allows the retrieval system to explore multiple semantic interpretations of the user's intent and select the most effective query formulation for matching with document content.
2Productivity
If question-answer pairings are used for document retrieval, then productivity is improved, but adaptability deteriorates as users cannot refine their queries interactively
Solution Approach 1:
The patent implements a dynamic retrieval process where the system initially performs rapid document retrieval using the user's query, then adaptively refines the results by presenting explainability scores and allowing users to adjust query parameters. This dynamic approach combines the speed of automated retrieval with the flexibility of interactive refinement.
Solution Approach 2:
The system provides feedback to users through explainability scores that indicate the relevance of retrieved documents and the importance of specific query terms. This feedback loop enables users to understand why certain documents were retrieved and to refine their queries accordingly, improving both productivity and adaptability.
3Measurement precision
If explainability scores are calculated for each word in queries and documents, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies explainability score calculation selectively rather than uniformly to all terms. The system identifies and focuses computational resources on key query terms and relevant document segments, calculating detailed explainability scores only where necessary to resolve ambiguity or improve retrieval accuracy, rather than performing exhaustive calculations across all text.
Data Source
AI summary
A system and method for productivity improvements in document comprehension is disclosed. The method includes steps of receiving a query statement for retrieving a subset of documents from a set of documents and generating a query vector to determine the similarity of the query statement with the documents in the set of documents using the query vector and the document vector to generate a document similarity score. The documents in the set of documents are ranked to generate a ranked set of documents. The method further determines at least one of a query explainability score and a document explainability score and provisions a user to select at least one word from at least one from the query statement and the document, extracting the subset of document from the ranked set of documents based on the selection of the user; and displaying the sub set of document.