Document processing employing probabilistic topic modeling of documents represented as text words transformed to a continuous space

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.

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.