Overview of the 3rd Author Profiling Task at PAN 2015

By Francisco Manuel Rangel Pardo, Fabio Celli, Paolo Rosso, Martin Potthast, Benno Stein, Walter Daelemans. Conference and Labs of the Evaluation Forum, 2015

302 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/87c9782e4d47bad4addde9a3a790145483fb6059

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Citations in the corpus

Bleaching Text: Abstract Features for Cross-lingual Gender Prediction (2018, background): “It is of interest to several applications including personalized machine translation, forensics, and marketing (Mirkin et al., 2015; Rangel et al., 2015).”
Analyzing Biases in Human Perception of User Age and Gender from Text (2016, background): “More recent author proﬁling experiments (Rangel et al., 2014; Rangel et al., 2015) revealed that gender can be well predicted from a large spectrum of textual features, ranging from paraphrase choice (Preot¸iuc-Pietro et al., 2016), emotions (Volkova and Bachrach, 2016), part-of-speech (Johannsen…”
Motivating Personality-aware Machine Translation (2015, methodology): “For this task we use the data from the 2015 PAN workshop (Rangel et al., 2015) which is labelled for author gender and personality traits.”
Gender Prediction for Authors of Russian Texts Using Regression And Classification Techniques (2016, methodology): “Different groups of text parameters were used which can be extracted using NLP tools such as content-based features (bag of words, words n-grams, dictionary words, slang words, ironic words, sentiment words, emotional words) and style-based features (frequency of punctuation marks, capital letters, ”
Gender Prediction for Chinese Social Media Data (2017, background): “, 1996); b) the collection of data with links to individual users is challenging, since Weibo (see below) requires users’ authorization before data collection; and c) there has been no published work (we are aware of) on this task, most work focusing on English and to some extent other Western langu”
Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style (2017, background): “In the last few years, more languages have received attention in the context of author proﬁling (Peersman et al., 2011; Nguyen et al., 2013; Rangel et al., 2015; Rangel et al., 2016), with the publication of the TwiSty corpus containing gender information on Twitter authors for six languages…”