Generation of Context-Aware Word Embedding Vectors for Given Semantic Properties of a Word Using Few Texts

An automatic context-aware embedding generation system, comprising:

• a pretrained context-aware natural language processing (NLP) circuitry for generating a sentence or higher-level embedding vector of a textual input expression in a multi-dimensional embedding space, the pretrained context-aware NLP circuitry comprising a neural network having at least one hidden layer;
• a context-aware embedding generator circuitry for generating embedding vectors at a word-level;
• and
• a datastore for storing context-aware word-level embedding vectors,
• wherein:
• the context-aware embedding generator circuitry is configured to receive the textual input expression and request the pretrained context-aware NLP circuitry to process the textual input expression;
• the pretrained context-aware NLP circuitry is configured to automatically parse the textual input expression into a plurality of input tokens, automatically generate context-aware information items in the at least one hidden layer corresponding to the plurality of input tokens, and automatically generate the sentence or higher-level embedding vector of the input textual expression in the multi-dimensional embedding space;
• the context-aware embedding generator circuitry is further configured to automatically aggregate the context-aware information items from the at least one hidden layer to derive a set of word-level context-aware embedding vectors in the multi-dimensional embedding space for the plurality of input tokens;
• and
• the context-aware embedding generator circuitry is further configured to store the set of word-level context-aware embedding vectors in the multi-dimensional embedding space for the plurality of input tokens in the datastore, wherein at least one word in the datastore corresponds to two or more word-level context-aware embedding vectors.