Embedding-Based Query Translation for Social Networks

The patent replaces the mechanical context-free grammar parsing system with a neural network-based embedding model. The LSTM classifier and translator networks process query embeddings to determine grammar query status and generate executable commands, substituting the rigid rule-based mechanical system with a learned probabilistic model that captures contextual nuances, thereby reducing over-triggering and under-triggering.

The patent transforms the query processing approach by changing from discrete grammar rules to continuous vector embeddings. Queries are converted to embedding vectors that capture semantic meaning, and the LSTM model processes these continuous representations to classify grammar queries and generate commands, fundamentally changing the parameter space from symbolic to continuous.

The patent creates a universal embedding-based translation model that handles all grammar query types without requiring separate templates. The LSTM translator network learns to map diverse query patterns to executable commands through training on embedded representations, making the system multi-functional and template-agnostic, thereby eliminating template management complexity while maintaining processing speed.

The patent uses embedding vectors as copies or representations of the actual queries. Instead of storing and matching against numerous text templates, the system creates vector copies of queries that capture their essential meaning, allowing efficient comparison and translation without the complexity of maintaining explicit template structures.