Deep Similarity Network for Search Query Word Vector Mapping
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Solution Overview
Problem
Conventional search query processing approaches face challenges in providing precise and relevant results due to varying and inconsistent terminology, as well as unnecessary language in user queries, leading to inaccurate search results.
Innovation Solution
The implementation of a deep learning-based system that extracts features from search queries using machine learning algorithms, generates word vectors through deep learning techniques, and maps them using a deep similarity network to provide accurate search results based on a corpus of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional query processing approaches are used, then the system is simple to operate, but the measurement precision of search results deteriorates due to varying and inconsistent terminology
Solution Approach 1:
The system performs preliminary action by pre-processing search queries through feature extraction and generating word vectors before the actual search matching process. Machine learning algorithms extract meaningful features from queries in advance, and deep learning techniques convert these features into word vectors that capture semantic relationships, preparing the data for more accurate subsequent processing without requiring complex real-time computations during search execution.
Solution Approach 2:
The patent introduces an intermediary mechanism by implementing a deep similarity network that acts as a mediator between the search query and the corpus data. This network contains learnable parameters that are trained to recognize semantic similarities, serving as an intelligent intermediary that bridges the gap between varied user terminology and standardized corpus terms, thereby improving measurement precision without directly modifying the core search infrastructure.
2Measurement precision
If deep learning techniques are applied to extract features and generate word vectors, then the measurement precision of search results improves, but the use of energy and computational resources increases
Solution Approach 1:
The system performs feature extraction and word vector generation as preliminary actions during an offline training phase or caching mechanism, rather than executing these computationally intensive operations for every online search query. By pre-computing and storing the mappings between query features and word vectors, the system significantly reduces real-time computational resource consumption while maintaining high measurement precision during actual search operations.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for incorporating data into search engines using deep learning mechanisms are provided herein. An example computer-implemented method includes extracting one or more features from a search query by applying one or more machine learning algorithms to the search query; generating one or more word vectors by applying at least one deep learning technique to the one or more extracted features; mapping the one or more generated word vectors to one or more words from a corpus of data by implementing at least one deep similarity network; and outputting one or more results in response to the search query, wherein the one or more results are based at least in part on the one or more words from the corpus to which the one or more generated word vectors were mapped.