Multi-Task Deep Neural Network for Semantic Search Representation
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Solution Overview
Problem
Current search engines often fail to capture semantic similarities between queries and documents when they use different words to express the same concept, leading to unsatisfactory search results.
Innovation Solution
A multi-task deep neural network system is employed for representation learning, mapping arbitrary text queries and documents into semantic vector representations in a low-dimensional latent space, combining operations for classification and ranking tasks to enhance query classification and web search performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search engine directly compares terms in the query with terms in documents, then the search process is simple and fast, but it fails to capture semantic similarities when different words express the same concept
Solution Approach 1:
The system performs preliminary action by pre-training deep neural networks to learn semantic representations of queries and documents before actual search execution. The DNN models are trained offline to map text into semantic vectors, so that during search, the system can directly compare these pre-computed semantic representations rather than performing complex semantic analysis in real-time.
Solution Approach 2:
The patent introduces an intermediary mechanism by using deep neural networks as a bridge between raw text and semantic comparison. The DNN models transform textual inputs into semantic vector representations, serving as an intermediary layer that enables meaningful similarity detection while keeping the actual search process computationally efficient.
2Measurement precision
If a search engine augments queries by finding synonyms and adding them to the query, then it can capture some semantic similarities, but it fails to uncover deeper semantic relationships and increases processing complexity
Solution Approach 1:
The patent replaces the mechanical synonym-matching system with a deep neural network-based semantic representation system. Instead of using rule-based synonym expansion and manual query augmentation, the system uses DNNs to automatically learn and represent semantic relationships, substituting complex mechanical processing with a more efficient neural representation approach.
Solution Approach 2:
The system changes parameters by transforming the search problem from term-matching space to semantic vector space. Instead of operating with discrete words and synonyms, the patent transforms all text into continuous semantic vectors, changing the fundamental parameter space in which similarity is measured and enabling more nuanced semantic comparisons.
3Reliability
If separate models are used for query classification and web search ranking, then each task can be optimized independently, but the system complexity increases and shared semantic representations are not utilized
Solution Approach 1:
The patent applies universality by designing a multi-task deep neural network that serves multiple functions simultaneously. The same DNN architecture and learned parameters are used for both query classification and web search ranking tasks, allowing the system to maintain high performance on both tasks while avoiding the complexity of separate models. The shared semantic representations benefit both tasks.
Solution Approach 2:
The system merges previously separate classification and ranking models into a unified multi-task deep neural network. By combining these tasks into a single architectural framework with shared layers and parameters, the patent reduces overall system complexity while maintaining the ability to perform both functions effectively through multi-task learning.
Data Source
AI summary
A system may comprise one or more processors and memory storing instructions that, when executed by one or more processors, configure one or more processors to perform a number of operations or tasks, such as receiving a query or a document, and mapping the query or the document into a lower dimensional representation by performing at least one operational layer that shares at least two disparate tasks.