TY - JOUR

T1 - Information-Theoretic method for classification of texts

AU - Ryabko, B. Ya

AU - Gus’kov, A. E.

AU - Selivanova, I. V.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - We consider a method for automatic (i.e., unmanned) text classification based on methods of universal source coding (or “data compression”). We show that under certain restrictions the proposed method is consistent, i.e., the classification error tends to zero with increasing text lengths. As an example of practical use of the method we consider the classification problem for scientific texts (research papers, books, etc.). The proposed method is experimentally shown to be highly efficient.

AB - We consider a method for automatic (i.e., unmanned) text classification based on methods of universal source coding (or “data compression”). We show that under certain restrictions the proposed method is consistent, i.e., the classification error tends to zero with increasing text lengths. As an example of practical use of the method we consider the classification problem for scientific texts (research papers, books, etc.). The proposed method is experimentally shown to be highly efficient.

UR - http://www.scopus.com/inward/record.url?scp=85031754667&partnerID=8YFLogxK

U2 - 10.1134/S0032946017030115

DO - 10.1134/S0032946017030115

M3 - Article

AN - SCOPUS:85031754667

VL - 53

SP - 294

EP - 304

JO - Problems of Information Transmission

JF - Problems of Information Transmission

SN - 0032-9460

IS - 3

ER -