Title: Topic Models and n-gram Language Models for Author Profiling - Notebook for PAN at CLEF 2015.
Abstract: Author profiling is the task of determining the attributes for a set of authors. This paper presents the design, approach, and results of our submission to the PAN 2015 Author Profiling Shared Task. Four corpora, each in a different language, were provided. Each corpus consisted of collections of tweets for a number of Twitter users whose gender, age and personality scores are know. The task was to construct some system capable of inferring the same attributes on as yet unseen authors. Our system utilizes two sets of text based features, n–grams and topic models, in conjunction with Support Vector Machines to predict gender, age and personality scores. We ran our system on each dataset and received results indicating that n-grams and topic models are effective features across a number of languages.
Publication Year: 2015
Publication Date: 2015-01-01
Language: en
Type: article
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Cited By Count: 5
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