Named Entity Recognition Using Capsule Networks

A system for named entity recognition, comprising at least one processor, the at least one processor configured to cause the system to at least perform:

• receiving, into a neural capsule embedding network as input, an embedding vector, wherein the embedding vector contains embeddings representing words in a natural language text;
• analyzing, by the neural capsule embedding network, the context of each word within the embedding vector considering tokens to the left and right of the word;
• through dynamic routing of capsules, by the neural capsule embedding network, converging to a final capsule layer mapping to each word in the input vector;
• generating, by the neural capsule embedding network, an output vector, wherein each output vector value:
• a) identifies if a word in the input is a named entity or not a named entity;
• b) if the word is a named entity, identifies what class the named entity belongs to.

A computer-implemented method for named entity recognition, comprising:

• receiving, into a neural capsule embedding network as input, an embedding vector, wherein the embedding vector contains embeddings representing words in a natural language text;
• analyzing, by the neural capsule embedding network, the context of each word within the embedding vector considering tokens to the left and right of the word;
• through dynamic routing of capsules, by the neural capsule embedding network, converging to a final capsule layer mapping to each word in the input vector;
• generating, by the neural capsule embedding network, an output matrix, wherein the output matrix:
• a) identifies if a word in the input is a named entity or not a named entity;
• b) if the word is a named entity, identifies what class the named entity belongs to.