Dynamic Word Embeddings for Domain-Specific Query Accuracy
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Solution Overview
Problem
Conventional word embedding systems face inaccuracies, inefficiencies, and inflexibilities in handling temporal ambiguities and context changes, requiring extensive resources and being limited in adaptability across domains, especially in identifying out-of-vocabulary words.
Innovation Solution
A dynamic word embedding system that generates domain-specific embeddings by concatenating numerical representations of domains with unique words, allowing for flexible adaptation across various domains and improving accuracy by embedding domain information directly into vector representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional word embedding systems use fixed-size vocabulary and train separate models for each context, then they can achieve domain-specific embeddings, but the device complexity and training resources increase significantly
Solution Approach 1:
The patent applies universality by using a single unified model that can handle multiple contexts and domains simultaneously. Instead of training separate models for each context, the system uses one model with dynamic word embeddings that adapt to different domains (e.g., medical, legal, technical) through context-aware vector representations, reducing model complexity while maintaining domain-specific accuracy
Solution Approach 2:
The patent implements dynamics by making word embeddings context-dependent and adaptable. The system dynamically adjusts word vectors based on the input context and domain, allowing the same model to generate accurate embeddings for different domains without retraining. This dynamic adaptation eliminates the need for fixed-size vocabulary constraints and separate model training
2Measurement precision
If conventional systems train separate models for each context and align them by fixed-size vocabulary, then they can capture context-specific meanings, but the training time and computational resources increase
Solution Approach 1:
The patent merges multiple context-specific models into a single unified model. By combining the functionality of separate context-specific models into one model with dynamic embedding capabilities, the system eliminates the need for multiple training processes and alignment procedures, significantly reducing training time and computational resources while maintaining context accuracy
Solution Approach 2:
The system performs preliminary action by pre-training a single unified model with diverse domain data before deployment. This pre-training enables the model to adapt to different contexts and domains without requiring separate training processes for each context, eliminating the time-consuming alignment step and reducing overall training time
3Measurement precision
If conventional systems use word-level embeddings, then they can represent semantic meaning, but they cannot identify out-of-vocabulary words
Solution Approach 1:
The patent applies another dimension by moving from static word-level embeddings to dynamic context-aware embeddings. The system adds a temporal and contextual dimension to word representations, allowing words to have different vector representations depending on the context and domain. This enables the model to handle out-of-vocabulary words by generating context-appropriate embeddings based on semantic relationships rather than relying on pre-defined vocabulary
4Measurement precision
If conventional systems are fixed to a particular embedding domain, then they can achieve high accuracy in that domain, but they cannot adapt to other domains
Solution Approach 1:
The patent applies parameter changes by making word embeddings dynamic and context-dependent. The system changes the embedding parameters (vector representations) based on the input context and domain, allowing the same model to achieve high accuracy across multiple domains. The embeddings adapt their parameters according to the specific domain requirements without requiring retraining or model changes
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating query results based on domain-specific dynamic word embeddings. For example, the disclosed systems can generate dynamic vector representations of words that include domain-specific embedded information. In addition, the disclosed systems can compare the dynamic vector representations with vector representations of query terms received as part of a search query. The disclosed systems can further identify one or more digital content items to provide as part of a query result that include words corresponding to the query terms based on the comparison of the vector representations. In some embodiments, the disclosed systems can also train a word embedding model to generate accurate vector representations of unique words.