Empiric Introduction to Light Stochastic Binarization

Abstract

We introduce a novel method for transformation of texts into short binary vectors which can be subsequently compared by means of Hamming distance measurement. Similary to other semantic hashing approaches, the objective is to perform radical dimensionality reduction by putting texts with similar meaning into same or similar buckets while putting the texts with dissimilar meaning into different and distant buckets. First, the method transforms the texts into complete TF-IDF, than implements Reflective Random Indexing in order to fold both term and document spaces into low-dimensional space. Subsequently, every dimension of the resulting low-dimensional space is simply thresholded along its 50th percentile so that every individual bit of resulting hash shall cut the whole input dataset into two equally cardinal subsets. Without implementing any parameter-tuning training phase whatsoever, the method attains, especially in the high-precision/low-recall region of 20newsgroups text classification task, results which are comparable to those obtained by much more complex deep learning techniques.