12. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform operations including: constructing a set of word embedding transforms by applying a dimensionality reduction algorithm to a term-document matrix constructed form a set of training documents and having matrix elements storing word frequencies scaled by a metric indicative of the frequencies of occurrence of the words in the set of documents to generate K-dimensional word vectors corresponding to the text words of the term-document matrix; performing a modeling method including: applying the set of word embedding transforms to transform text words of a set of documents into K-dimensional word vectors in order to generate the sets or sequences of word vectors representing the documents where K is an integer greater than or equal to two; and learning a probabilistic topic model comprising M components representing M topics using the sets or sequences of word vectors representing the documents of the set of documents wherein the learned probabilistic topic model operates to assign probabilities for the topics of the probabilistic topic model to an input set or sequence of K-dimensional embedded word vectors; and performing a document processing method including: applying the set of word embedding transforms to transform text words of an input document into corresponding K-dimensional word vector in order to generate a set or sequence of word vectors representing the input document; and applying the learned probabilistic topic model to the set or sequence of word vectors representing the input document in order to generate a topic model representation of the input document comprising one of (1) a vector or histogram of topic probabilities representing the input document or (2) one or more Fisher vectors representing the input document.
12. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform operations including: constructing a set of word embedding transforms by applying a dimensionality reduction algorithm to a term-document matrix constructed form a set of training documents and having matrix elements storing word frequencies scaled by a metric indicative of the frequencies of occurrence of the words in the set of documents to generate K-dimensional word vectors corresponding to the text words of the term-document matrix; performing a modeling method including: applying the set of word embedding transforms to transform text words of a set of documents into K-dimensional word vectors in order to generate the sets or sequences of word vectors representing the documents where K is an integer greater than or equal to two; and learning a probabilistic topic model comprising M components representing M topics using the sets or sequences of word vectors representing the documents of the set of documents wherein the learned probabilistic topic model operates to assign probabilities for the topics of the probabilistic topic model to an input set or sequence of K-dimensional embedded word vectors; and performing a document processing method including: applying the set of word embedding transforms to transform text words of an input document into corresponding K-dimensional word vector in order to generate a set or sequence of word vectors representing the input document; and applying the learned probabilistic topic model to the set or sequence of word vectors representing the input document in order to generate a topic model representation of the input document comprising one of (1) a vector or histogram of topic probabilities representing the input document or (2) one or more Fisher vectors representing the input document. 15. The non-transitory storage medium of claim 12 , wherein the topic model representation of the input document comprises one or more Fisher Vectors representing the input document.