Sub-word Embeddings for Semantic Search Accuracy
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Solution Overview
Problem
Traditional data management systems fail to accurately understand the intent behind user queries containing uncommon terms or misspellings, leading to inadequate assistance and increased resource consumption.
Innovation Solution
A data management system utilizing deep learning-based sub-word embeddings to break down terms into sub-words, convert them into vectors, and identify related terms in a vector space, providing accurate assistance by generating augmented query data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management systems use predefined lists of synonyms and misspellings, then they can identify some user queries correctly, but they fail to understand queries containing uncommon terms or misspellings not in the list
Solution Approach 1:
The system segments terms into sub-words (character n-grams) to create a more granular representation. This allows the model to break down unfamiliar terms into component parts that can be independently analyzed and matched against the vector space, enabling recognition of uncommon terms and misspellings that would not be captured by whole-word matching.
Solution Approach 2:
The system transforms terms from discrete categorical values into continuous vector representations using sub-word embeddings. This parameter transformation allows for semantic similarity calculation based on vector distance, enabling the system to identify conceptually related terms even when the exact word or spelling doesn't match, thereby improving adaptability to uncommon terminology.
2Productivity
If traditional systems perform multiple fruitless searches when users enter unrecognized terms, then they consume large amounts of computing resources, but they still fail to provide relevant assistance
Solution Approach 1:
The system performs preliminary processing of terms into sub-word embeddings before the search operation. By pre-computing vector representations of sub-words and storing them in a vector space, the system prepares the data structure in advance to enable efficient similarity-based matching, avoiding the need for multiple fruitless sequential searches.
Solution Approach 2:
The system replaces the mechanical sequential search approach with a vector-space-based similarity search. Instead of checking terms against fixed lists or performing multiple sequential searches, the system uses vector distance calculations to efficiently identify semantically related terms, significantly reducing computing resource consumption.
3Reliability
If users make telephone calls to speak with experts, then they receive accurate assistance, but the process is very expensive and time consuming
Solution Approach 1:
The system enables self-service by automatically understanding user queries through sub-word embedding analysis and providing relevant assistance documents without requiring human expert intervention. The system serves itself by using the vector space to identify and retrieve appropriate help resources, making the assistance process autonomous, immediate, and scalable.
Data Source
AI summary
A method and system provides assistance to users of a data management system. The method and system trains an analysis model with a machine learning process to generate sub-word embeddings corresponding to vectorized representations of portions of a search term entered by a user. The method and system generates augmented query data based on the sub-word embeddings. The method and system provides assistance to the user based on the augmented query data.