UAMCLyR at RepLab 2014: Author Profiling Task

This paper describes the participation of the Language and Reasoning Group of UAM at RepLab 2014 Author Profiling evaluation lab. This task involves author categorization and author ranking subtasks. Our method for author categorization uses a supervised approach based on the idea that we can use the information on Twitter’s user profile, then by means of employing an attribute selection techniques we can extract attributes that are the most representative from each user’s activity domain. For the author ranking subtask we use a two step chained method that uses stylistics attributes (e.g. lexical richness, language complexity) and behavioral attributes (e.g. posts’ frequency, directed tweets) extracted from the users’ profile and the posts. We use these attributes in conjunction with a Markov Random Fields for improving an initial ranking given by the confidence of Support Vector Machine classification algorithm. Obtained results are encouraging and motivate us to keep working on the same ideas.