20. A method comprising: (i) generating a probabilistic topic model comprising M topics by operations including: constructing a set of word embedding transforms used in the embedding operation by applying a dimensionality reduction algorithm to a term frequency-inverse document frequency matrix constructed form a set of training documents to generate K-dimensional word vectors corresponding to the text words of the term-document matrix; 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 sets or sequences of word vectors representing the documents of the set of documents where K is an integer greater than or equal to two; and learning the probabilistic topic model 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 (ii) processing an input document by operations including: applying the set of word embedding transforms to transform text words of the input document into corresponding K-dimensional word vectors 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 to compute a set of statistics representing the input document based on the learned topic model; wherein the operations (i) and (ii) are performed by an electronic data processing device.
23. The method of claim 20 , further comprising: identifying one or more similar documents that are similar to the input document as measured by the computed statistics representing the input document; wherein the identifying is performed by an electronic data processing device.