Probabilistic word embeddings for text classification

A computing system comprising:

• one or more computer processors;
• and
• one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the computing system to perform operations comprising:
• receiving, from a client device, a first message transmitted as part of a communication session, the first message including at least a first keyword;
• determining a first embedding probability distribution representing the first keyword;
• determining a first embedding value for the first keyword based on the first embedding probability distribution;
• using the first embedding value as input into each mathematical function in a set mathematical functions, yielding a first set of coefficient values for the first keyword, each respective mathematical function from the set of mathematical functions corresponding to a respective classification label from a set of classification labels and defining a continuous surface, each respective mathematical function determined from embedding values for a set of known keywords, distribution variance values for the set of known keywords, and a subset of coefficient values for the set of known keywords, the respective subset of coefficient values corresponding to the respective classification label;
• for each classification label from the set of classification labels, calculating a probability score based on the first set of coefficient values, yielding a set of probability scores for the first message;
• and
• assigning a first classification label from the set of classification labels to the first message based on the set of probability scores for the first message.

A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of a computing system, cause the computing system to perform operations comprising:

• receiving, from a client device, a first message transmitted as part of a communication session, the first message including at least a first keyword;
• determining a first embedding probability distribution representing the first keyword;
• determining a first embedding value for the first keyword based on the first embedding probability distribution;
• using the first embedding value as input into each mathematical function in a set mathematical functions, yielding a first set of coefficient values for the first keyword, each respective mathematical function from the set of mathematical functions corresponding to a respective classification label from a set of classification labels and defining a continuous surface, each respective mathematical function determined from embedding values for a set of known keywords, distribution variance values for the set of known keywords, and a subset of coefficient values for the set of known keywords, the respective subset of coefficient values corresponding to the respective classification label;
• for each classification label from the set of classification labels, calculating a probability score based on the first set of coefficient values, yielding a set of probability scores for the first message;
• and
• assigning a first classification label from the set of classification labels to the first message based on the set of probability scores for the first message.