This paper describes the methods we have employed to solve the author profiling task at PAN-2014. Our goal was to rely mainly on features from Information Retrieval to identify the age group and the gender of the author of a given text. We describe the features, the classification algorithms employed, and how the experiments were run. Also, we provide an analysis of our results compared to othe...

Author profiling deals with the study of various profile dimensions of an author such as age and gender. This work describes our methodology proposed for the task of cross-genre author profiling at PAN 2016. We address gender and age prediction as a classification task and approach this problem by extracting stylistic and lexical features for training a logistic regression model. Furthermore, w...

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment evaluate our systems several tasks in Spanish English. addition, the effect of both hypothesis size sample. find entailment-based ou...

This paper gives a brief description on the methods adopted for the task of author-profiling as part of the competition PAN 2016 [1]. Author profiling is the task of predicting the author’s age and gender from his/her writing. In this paper, we follow a two-level ensemble approach to tackle the cross-genre author profiling task where training documents and testing documents are from different g...

This paper describes neural network models that we prepared for the author profiling task of PAN@CLEF 2017. In previous PAN series, statistical models using a machine learning method with a variety of features have shown superior performances in author profiling tasks. We decided to tackle the author profiling task using neural networks. Neural networks have recently shown promising results in ...

Author profiling aims at identifying the authors’ traits on the basis of their sociolect aspect, that is, how language is shared by them. This work describes the system submitted by Symanto Research for the PAN 2017 Author Profiling Shared Task. The current edition is focused on language variety and gender identification on Twitter. We address these tasks by exploiting the morphology and semant...

This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author t...