Semantic Matching Model for Search Result Relevance
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Solution Overview
Problem
Current search engines face challenges in providing relevant search results due to the increasing volume of information online, as they struggle to accurately match user queries with the most pertinent information resources.
Innovation Solution
A method and apparatus that utilize a pre-trained semantic matching model based on syntax trees to calculate the similarity between search statements and search results, sorting results for improved relevance, and a training model established using a recursive neural network to enhance semantic matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines simply retrieve and display search results based on keyword matching, then the search process is fast and simple, but the relevance and accuracy of search results deteriorates as information volume increases
Solution Approach 1:
The patent pre-trains a semantic matching model offline using large amounts of corpus data before actual search operations. This preliminary training enables the model to understand semantic relationships between words and generate accurate similarity scores during online search, resolving the contradiction by preparing the complex matching capability in advance rather than computing it in real-time
Solution Approach 2:
The patent segments the semantic matching process into distinct components: syntax tree generation from search queries, syntax tree generation from search results, and similarity calculation between these trees. This segmentation allows each component to be optimized independently while maintaining overall system efficiency
2Reliability
If search engines use simple keyword matching algorithms, then the processing speed is fast, but the ability to understand user intent and provide accurate results deteriorates
Solution Approach 1:
The semantic matching model is pre-trained offline on large corpora to learn semantic relationships between words and phrases. This preliminary action stores semantic knowledge in the model's parameters, enabling fast and accurate similarity calculations during actual search operations without requiring complex real-time processing
Solution Approach 2:
The patent replaces traditional mechanical keyword matching algorithms with a neural network-based semantic matching model. This substitution enables the system to understand user intent and semantic relationships rather than simply comparing keywords, significantly improving search accuracy while maintaining reasonable processing speeds
3Measurement precision
If the semantic matching model uses detailed syntax tree analysis, then the matching precision improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent generates syntax trees for both search queries and search results to capture semantic structure, but uses these trees selectively - primarily for calculating similarity scores rather than for all subsequent processing steps. This partial application of detailed analysis maintains accuracy while controlling computational overhead
Solution Approach 2:
The patent transforms the semantic matching problem into a parameter optimization problem by using neural network models with learnable parameters. The syntax tree analysis results are converted into numerical features that feed into the similarity calculation model, allowing the system to balance detail and efficiency through parameter tuning rather than exhaustive analysis
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for processing information. A specific implementation of the method includes: acquiring a search result set related to a search statement inputted by a user; parsing the search statement to generate a first syntax tree, and parsing a search result in the search result set to generate a second syntax tree set; calculating a similarity between the search statement and the search result in the search result set using a pre-trained semantic matching model on the basis of the first syntax tree and the second syntax tree set, the semantic matching model being used to determine the similarity between the syntax trees; and sorting the search result in the search result set on the basis of the similarity between the search statement and the search result in the search result set, and pushing the sorted search result set to the user.