Context Disambiguation Using Topic Embeddings and N-Sphere Retrieval
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Solution Overview
Problem
Existing retrieval systems struggle to handle ambiguous user inputs by providing a diverse selection of relevant documents while maintaining semantic and syntax relevancy, often resulting in less relevant or conflicting responses due to reliance on distance metrics.
Innovation Solution
The Ambiguous-Agnostic Context Disambiguation (AACD) system uses high-dimensional vector spaces and embeddings to identify relevant document fragments by prioritizing semantic and syntax relevancy, constructing n-spheres to retrieve documents within a search radius, ensuring both breadth and relevance in responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If distance metrics (cosine distance or Euclidean distance) are used to compare embeddings between user input and documents, then retrieval speed and simplicity are improved, but the ability to handle ambiguous user inputs and provide diverse relevant documents deteriorates
Solution Approach 1:
The patent segments the retrieval process into multiple stages: first identifying candidate documents using distance metrics, then applying re-ranking with advanced similarity measures (cosine similarity, Jaccard similarity, Levenshtein distance) to handle ambiguity. This multi-stage approach maintains speed while improving adaptability to ambiguous inputs.
Solution Approach 2:
The patent changes the parameters used for document evaluation from simple distance metrics to multiple similarity parameters including cosine similarity, Jaccard similarity, and Levenshtein distance. By adjusting and combining these parameters, the system can adapt to ambiguous user inputs while maintaining retrieval efficiency.
2Measurement precision
If retrieval systems prioritize depth (accuracy of documents relating to user input), then response precision is improved, but the ability to provide diverse document selections deteriorates
Solution Approach 1:
The patent implements a dynamic retrieval system that adjusts between depth and breadth based on input characteristics. For ambiguous inputs, the system dynamically expands the search to include diverse documents with moderate similarity scores, while for clear inputs, it focuses on high-precision matches. This dynamic adjustment is achieved through the re-ranking mechanism that combines multiple similarity measures.
Solution Approach 2:
The patent retrieves more documents than traditionally necessary (excessive action) and then applies re-ranking to select the most relevant ones. By initially retrieving a broader set of documents including those with moderate similarity scores, the system ensures diversity is not lost, then uses multi-parameter evaluation to identify the precise matches needed.
3Adaptability or versatility
If multiple similarity measures (cosine similarity, Jaccard similarity, Levenshtein distance) are combined for re-ranking, then handling of ambiguous inputs is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into two phases: a fast initial retrieval phase using simple distance metrics, and a more computationally intensive re-ranking phase that applies multiple similarity measures only to candidate documents. This segmentation reduces overall computational complexity while maintaining the ability to handle ambiguous inputs effectively.
Solution Approach 2:
The patent performs preliminary filtering using distance metrics before applying the more computationally expensive similarity measures. By pre-identifying candidate documents that meet basic criteria, the system reduces the number of documents that require intensive multi-measure evaluation, thereby lowering overall computational complexity while preserving adaptability to ambiguous inputs.
Data Source
AI summary
In one embodiment, a method includes receiving a user input including a text string from a client system, identifying a first topic embedding from among multiple topic embeddings based on an embedding associated with the user input, wherein each embedding associated with the user input or each topic embedding is withing an n-dimensional embedding space, identifying first entities associated with the user input, identifying entity embeddings based on the first entities, wherein each entity embedding is within the n-dimensional embedding space, generating a respective n-sphere based on each entity embedding and the first topic, wherein each n-sphere is associated with a respective set of document fragments, retrieving one or more sets of document fragments associated with the n-spheres, and sending instructions for presenting the retrieved sets of document fragments to the client system responsive to the user input.