참고문헌 (12)

1. Francisco Rangel, Paolo Rosso, Martin Potthast, and Benno Stein. Overview of the 5th author profiling task at pan 2017: Gender and language variety identification in twitter. Working Notes Papers of the CLEF, 2017. 

2. Angelo Basile, Gareth Dwyer, Maria Medvedeva, Josine Rawee, Hessel Haagsma, and Malvina Nissim. N-gram: New groningen author-profiling model. arXiv preprint arXiv:1707.03764, 2017. 

3. Matej Martinc, Iza ？krjanec, Katja Zupan, and Senja Pollak. Pan 2017: Author profiling-gender and language variety prediction. Cappellato et al.[13], 2017. 

4. Yasuhide Miura, Tomoki Taniguchi, Motoki Taniguchi, and Tomoko Ohkuma. Author profiling with word+ character neural attention network. Cappellato et al.[13]. 

5. Proceedings of the Workshop on Stylistic Variation Litvinova 69 2017 10.18653/v1/W17-4909 Differences in type- token ratio and part-of-speech frequencies in male and female russian written texts 

   

6. 10.1109/FRUCT.2016.7584767 Tatiana Litvinova, Olga Litvinlova, Olga Zagorovskaya, Pavel Seredin, Aleksandr Sboev, and Olga Romanchenko. “ruspersonality”: A russian corpus for authorship profiling and deception detection. In Intelligence, Social Media and Web (ISMW FRUCT), 2016 International FRUCT Conference on, pages 1-7. IEEE, 2016. 

   

7. RusProfiling Lab. Rusprofiling corpus of russian texts. [online], 2017. http://rusprofilinglab.ru/rusprofiling-at-pan/corpus/. 

8. EACL Kutuzov 2017 99 2017 Building web-interfaces for vector semantic models with the webvectors toolkit 

9. Nal Kalchbrenner, Ivo Danihelka, and Alex Graves. Grid long short-term memory. arXiv preprint arXiv:1507.01526, 2015. 

10. Eliyahu Kiperwasser and Yoav Goldberg. Simple and accurate dependency parsing using bidirectional lstm feature representations. arXiv preprint arXiv:1603.04351, 2016. 

11. Lin Wu, Chunhua Shen, and Anton van den Hengel. Personnet: Person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255, 2016. 

12. 10.1109/CSCI.2016.0210 Aleksandr Sboev, Tatiana Litvinova, Irina Voronina, Dmitry Gudovskikh, and Roman Rybka. Deep learning network models to categorize texts according to author’s gender and to identify text sentiment. In Computational Science and Computational Intelligence (CSCI), 2016 International Conference on, pages 1101-1106. IEEE, 2016.