@inproceedings{8c38bb4e79494d9cb7637491322667df,
title = "The winning approach to cross-genre gender identification in Russian at RUSProfiling 2017",
abstract = "We present the CIC systems submitted to the 2017 PAN shared task on Cross-Genre Gender Identification in Russian texts (RUSProfiling). We submitted five systems. One of them was based on a statistical approach using only lexical features, and other four on machine-learning techniques using some combinations of gender-specific Russian grammatical features, word and character n-grams, and suffix n-grams. Our systems achieved the highest weighted accuracy across all the test datasets, occupying the first four places in the ranking.",
author = "I. Markov and H. G{\'o}mez-Adorno and G. Sidorov and A. Gelbukh",
year = "2017",
language = "English",
volume = "2036",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "20--24",
editor = "P. Majumder and J. Sankhavara and M. Mitra and P. Mehta",
booktitle = "FIRE 2017 - Working Notes of FIRE 2017 - Forum for Information Retrieval Evaluation",
note = "2017 Working Notes of Forum for Information Retrieval Evaluation, FIRE 2017 ; Conference date: 08-12-2017 Through 10-12-2017",
}