Vector Representation of Context Networks of Latent Topics

Transforming of text documents to real vectors is an essential step for text mining tasks such as classification, clustering and information retrieval. The extracted vectors serve as inputs for data mining models. Large vocabularies of natural languages imply a high dimensionality of input vectors; hence a substantial dimensionality reduction has to be made. We propose a new approach to a vector representation of text documents. Our representation takes into account an order of latent topics that generate observed words; an extracted document vector includes information about the adjacency of words in a document. We experimentally proved that the proposed representation enables to build document classifiers of higher accuracy using shorter document vectors. Short but informative document vectors enable to save memory for storing data, to use simpler models that learn faster and to significantly reduce an overfit effect.