Overview of the Author Profiling Task at PAN 2013
By Francisco Manuel Rangel Pardo, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. 2013
321 citations recorded by the source corpus.
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- Barbara Plank
- Sebastian Ruder
- Anders Søgaard
- Dong Nguyen
- Eva Vanmassenhove
- Dirk Hovy
- A. Seza Doğruöz
- Rob van der Goot
Citations in the corpus
- Getting Gender Right in Neural Machine Translation (2019, background): “The increasing amount of work on automatic author classification (or ‘au-thor profiling’) reaching relatively high accuracies on domain-specific data corroborates these findings (Rangel et al., 2013; Santosh et al., 2013).”
- 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”
- Learning Multimodal Gender Profile using Neural Networks (2017, background): “Since 2013 tasks on author profiling have been organized at the PAN lab of CLEF (Rangel et al., 2013).”
- Reusable workflows for gender prediction (2018, background): “PAN2014-blogs (Rangel et al., 2014)3: the dataset consists of 2,278 blogs by 146 authors ( 73 male and 73 female).”
- 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.”
- Computational Sociolinguistics: A Survey (2016, methodology): “Rangel et al. (2013) adopted this approach in the Author Profiling task at PAN 2013.”
- 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).”
- Adversarial Removal of Demographic Attributes Revisited (2019, methodology): “We use the following datasets for these experiments: PAN14 (Rangel et al., 2014)4 - we include the English data from PAN14 for the following domains: BLOGS, hotel REVIEWS, and Social Media (SOME).”