Double-Level Ranking with Topic Snippets for Contextual Search
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Solution Overview
Problem
Conventional information retrieval systems prioritize keyword frequency over contextual relevance, leading to inaccurate representation of subtopics and context misrepresentation in lengthy, complex documents, especially when coupled with AI systems.
Innovation Solution
Implementing a novel indexing and ranking process that uses a topic parser to create sub-topic snippets and employs a large language model (LLM) for paragraph-level embeddings, followed by a dynamic re-ranking based on cosine similarity to ensure contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword frequency is used for relevance scoring, then the retrieval process is simple and fast, but the contextual relevance and semantic understanding are poor
Solution Approach 1:
The patent segments documents into multiple topics using a topic parser, creating topic-specific snippets from each document. This segmentation allows the system to analyze and score relevance at the topic level rather than treating entire documents as single units, thereby improving measurement precision while maintaining computational efficiency through targeted processing of smaller text segments.
Solution Approach 2:
The patent introduces an intermediary re-ranking process that operates between initial keyword-based retrieval and final result presentation. This intermediary step uses topic-level semantic analysis to adjust and refine the relevance scores of retrieved documents, improving accuracy without completely replacing the efficient keyword-based initial retrieval mechanism.
2Adaptability or versatility
If keyword overloading is allowed in lengthy documents, then more topics are covered, but the relevance assessment becomes inaccurate
Solution Approach 1:
The topic parser divides lengthy multi-topic documents into distinct topic segments, each represented as a separate snippet. This segmentation prevents keyword overloading from skewing overall document relevance scores, as each topic is evaluated independently. The system can thus cover multiple topics (high adaptability) while maintaining accurate relevance assessment for each specific topic through separate scoring.
Solution Approach 2:
The patent applies local quality by evaluating different parts (topics) of a document with different relevance scores based on their specific content. Instead of applying a single global relevance score to the entire document, the system assigns topic-specific scores that reflect the local relevance of each snippet, thereby improving measurement precision for multi-topic documents.
3Productivity
If conventional snippet generation is used from large documents, then processing is fast, but contextually appropriate snippets cannot be produced
Solution Approach 1:
The topic parser segments large documents into topic-specific snippets based on actual topic boundaries rather than arbitrary fixed-length divisions. This segmentation approach maintains processing speed by working with smaller units while improving contextual accuracy, as each snippet represents a coherent topic segment with proper context rather than random portions of large documents.
Data Source
AI summary
This invention relates to systems and methods for performing double-level ranking of documents. The system implements methods for retrieving documents based on a pre-processed user query to generate a document level ranking of one or more documents that are determined to be relevant. The system implements methods for aggregating one or more sub-topic snippets from the document level ranked documents. The system further implements methods for generating a topic level ranking of the one or more sub-topic snippets. Once topic level ranking of the one or more sub-topic snippets has been performed, the system implements methods for transmitting the topic level ranked one or more sub-topic snippets to a user associated with the user query.