The Winning Approach to Cross-Genre Gender Identification in Russian at RUSProfiling 2017

@inproceedings{Markov2017TheWA,
  title={The Winning Approach to Cross-Genre Gender Identification in Russian at RUSProfiling 2017},
  author={Ilia Markov and Helena G{\'o}mez-Adorno and Grigori Sidorov and Alexander F. Gelbukh},
  booktitle={FIRE},
  year={2017}
}
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 genderspecific 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… CONTINUE READING

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