2. The method of claim 1 , where generating a category-topic model further includes: determining, from the co-occurrence relationship between the topic and the category label, a first probability of the topic being associated with a document given that the category label is associated with the document; and determining, from a co-occurrence relationship between the sequence and the topic, a second probability of the sequence occurring in the document given that the topic is associated with the document.
1. A method comprising: receiving a plurality of documents of text, wherein each document is associated with one or more category labels and includes one or more sequences of one or more words; determining a plurality of topics from the plurality of documents, wherein each topic represents a subdivision of a respective category label; performing a plurality of sampling iterations to generate a category-topic model that represents co-occurrence relationships between sequences and topics and co-occurrence relationships between topics and categories, wherein performing each of the plurality of sampling iterations comprises, for each sequence in each of the plurality of documents: sampling a category label for the sequence from the category labels associated with the document that includes the sequence; sampling a topic for the sequence; and updating current values of representations of the co-occurrence relationships based on the category label and the topic sampled for the sequence.