Semantic Vector Mapping for Emerging Query Terms
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Solution Overview
Problem
Existing techniques for mapping terms to vector representations in a semantic space are inefficient and inaccurate, particularly when dealing with query terms that were not pre-calculated, leading to significant computational resource consumption and failure to capture the meaning of emerging interest terms.
Innovation Solution
A system that pre-computes vector representations for a subset of predicted query terms using frequency-based and database-based techniques, and generates correlation data for terms not in the pre-computed set, allowing for efficient and accurate determination of vector representations during runtime using a neural network model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector representations are pre-computed for all terms in the document corpus, then mapping accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The system pre-computes and stores vector representations for a subset of predicted query terms in advance, so that when actual queries arrive, the most likely terms are already prepared. This preliminary action reduces the computational burden during runtime while maintaining high mapping accuracy for frequently occurring terms.
Solution Approach 2:
Instead of computing vectors for all terms in the corpus (excessive action), the system computes vectors only for a strategically selected subset of predicted query terms (partial action). This partial computation approach achieves sufficient accuracy for practical purposes while dramatically reducing computational resource consumption.
2Speed
If vector representations are pre-computed for all terms, then mapping speed during runtime is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary computation of vector representations for predicted query terms and stores them in a data structure. During runtime, when actual queries arrive, the system can quickly retrieve pre-computed vectors without performing complex computations, thereby increasing mapping speed while keeping the runtime system relatively simple.
Solution Approach 2:
The system creates a simplified representation (copy) of the complex vector computation process by pre-computing and storing results. This allows the runtime system to simply retrieve and use these pre-computed copies rather than performing the full computation again, increasing speed without requiring complex runtime computation logic.
3Productivity
If frequency-based pre-computation is used for common terms, then computational efficiency is improved, but accuracy for emerging interest terms deteriorates
Solution Approach 1:
The system dynamically adapts its behavior based on query characteristics. For common terms, it uses pre-computed vectors for efficiency. For emerging or uncommon terms that lack pre-computed representations, it falls back to alternative methods (such as using context from surrounding terms or re-computation) to maintain accuracy. This dynamic approach balances efficiency and accuracy across different term types.
Solution Approach 2:
The system introduces an intermediary mechanism that bridges the gap between pre-computed common terms and emerging terms. When a query term is not found in the pre-computed set, the intermediary fallback mechanism (using contextual information or alternative representation methods) provides a way to still generate accurate representations without requiring pre-computation, thus maintaining accuracy for emerging terms while preserving efficiency for common terms.
Data Source
AI summary
A method and system is provided for mapping a term to a vector representation in a semantic space. Provided techniques allow for efficient and accurate determination of vector representations for query terms that are terms of emerging interest or are otherwise not included in a set of terms for which vector representations are pre-calculated.