Topic models

A system for training a topic model, comprising:

• one or more processing units;
• and
• memory comprising instructions that when executed by at least one of the one or more processing units, perform a method comprising:
• for a document within a document corpus:
• receiving a document representation of the document and features of the document, the document representation comprising a frequency of word occurrences within the document;
• processing the document representation and the features using a topic model, the processing comprising:
• specifying a feature/topic parameter for a feature of the document, the feature/topic parameter specifying a probability of the feature being indicative of a first topic, the feature/topic parameter based upon a first uncertainty measure;
• and
• specifying a document/word/topic parameter for a word within the document, the document/word/topic parameter specifying a probability of the word being indicative of a second topic, the document/word/topic parameter based upon a second uncertainty measure;
• and
• training the topic model based upon the feature/topic parameter and the document/word/topic parameter.

A computer readable medium comprising instructions that when executed, perform a method for predicting a topic of a document, the method comprising:

• processing a document representation of a document and features of the document using a topic model, the processing comprising:
• determining a feature/topic prediction for a feature of the document, the feature/topic prediction specifying a probability of the feature being indicative of a first topic;
• determining a word/topic prediction for a word within the document, the word/topic prediction specifying a probability of the word being indicative of a second topic;
• and
• determining a document/topic prediction for the document based upon the feature/topic prediction and the word/topic prediction, the document/topic prediction specifying a probability of the document being indicative of a third topic.