TY - GEN
T1 - The winning approach to cross-genre gender identification in Russian at RUSProfiling 2017
AU - Markov, I.
AU - Gómez-Adorno, H.
AU - Sidorov, G.
AU - Gelbukh, A.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85041441696
UR - https://www.scopus.com/pages/publications/85041441696#tab=citedBy
M3 - Conference contribution
VL - 2036
T3 - CEUR Workshop Proceedings
SP - 20
EP - 24
BT - FIRE 2017 - Working Notes of FIRE 2017 - Forum for Information Retrieval Evaluation
A2 - Majumder, P.
A2 - Sankhavara, J.
A2 - Mitra, M.
A2 - Mehta, P.
PB - CEUR Workshop Proceedings
T2 - 2017 Working Notes of Forum for Information Retrieval Evaluation, FIRE 2017
Y2 - 8 December 2017 through 10 December 2017
ER -