RusProfiling at PAN

 

WELCOME

Author profiling consists of predicting an author’s demographics (e.g. age, gender, personality) from his/her writing, with gender identification being the most popular task (Rangel et al., 2013; Celli et al., 2014; Rangel et al., 2015; Litvinova et al. 2016a; Litvinova et al. 2016b, Rangel et al., 2016;  Sboev et al. 2016a; Sboev et al. 2016b). Author profiling tasks are popular among participants of PAN which is a series of scientific events and shared tasks on digital text forensics (http://pan.webis.de/index.html). Shared tasks are computer science events that invite researchers and practitioners to work on a specific problem of interest, the task.

Slavic languages, however, are less investigated from author profiling standpoint and have never been presented at PAN.

This year we introduce a PAN shared task on Cross-genre Gender Identification in Russian texts (RusProfiling shared task) where we will provide as training dataset tweets and as test dataset tweets, Facebook posts, as well as reviews, texts describing images, or letters to a friend.

RusProfiling shared task will be held in conjunction with FIRE 2017 Forum for Information Retrieval Evaluation.

Accepted papers describing the results of the experiments will be published in CEUR-WS workshop proceedings. CEUR-WS are indexed by Scopus and DBLP: see http://ceur-ws.org/HOWTOSUBMIT.html.

We cordially invite all researchers and practitioners from all fields to participate

in this year’s PAN @ FIRE shared task!

 

Celli F., Lepri B., Biel J.I., Gatica-Perez D., Riccardi G., Pianesi F. (2014). The workshop on computational personality recognition 2014. Proc. of the ACM Int. Conf. on Multimedia. pp. 1245–1246.

Litvinova T., Litvinlova, O., Zagorovskaya O., Seredin P., Sboev A., Romanchenko O. Ruspersonality: A Russian corpus for authorship profiling and deception detection. In: Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016. 2016a. IEEE.

Litvinova T., Seredin P., Litvinova O., Zagorovskaya O., Sboev A., Gudovskih D., Moloshnikov I., Rybka R. Gender prediction for authors of Russian texts using regression and classification techniques. In: Jaume Baixeries, Dmitry I. Ignatov, Dmitry Ilvovsky, Alexander Panchenko (Eds.) Proceedings of The 3d International Workshop on Concept Discovery in Unstructured Data (CDUD 2016). CEUR Workshop Proceedings. CEUR-WS.org, 2016b, vol. 1625, pp. 44-53.

Rangel F., Celli F., Rosso M., Potthast M., Stein B., Daelemans W. (2015). Overview of the 3rd Author Profiling Task at PAN 2015. CLEF 2015 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1391.

Rangel F., Rosso P., Koppel M., Stamatatos E., Inches G. Overview of the Author Profiling Task at PAN 2013. In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179

Rangel F., Rosso P., Verhoeven B., Daelemans W., Pottast M., Stein B. Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations. In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784.

Sboev A., Litvinova T., Gudovskikh D., Rybka R., Moloshnikov I. Machine Learning Models of Text Categorization by Author Gender Using Topic-independent Features. Procedia Computer Science. 2016a. Vol. 101. P. 135-142.

Sboev A., Litvinova T., Voronina I., Gudovskikh D., Rybka R. Deep Learning Network Models to Categorize Texts According to Author’s Gender and to Identify Text Sentiment. In: Proceedings of 2016 International Conference on Computational Science and Computational Intelligence (CSCI 2016). Las Vegas, NV, USA, 2016b. 2016. IEEE.

 

Task Coordinators