Christian Sánchez-Sánchez

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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(More)
In this article we deal with the Topic Detection and Priority Detection subtasks from RepLab 2013, trying clustering and classification methods as well as term selection techniques in order to know its performance in two sub collections of tweets: single and extended (single tweet plus derived tweets). Our tests show good performance in spite of we used(More)
In this paper we describe the participation of the Language and Reasoning group from UAM-C in the context of the Cross Language SOurce COde re-use competition (CL-SOCO 2015). We proposed a representation of source code pairs by using five high level features; namely: i) lexical feature, ii) stylistic feature, iii) comments feature, iv) programmer's text(More)
Source code plagiarism can be identified by analyzing several and diverse views of a pair of source code. In this paper we present three representations from lexical and structural views of a given source code. We attempt to show that different representations provide diverse information that can be useful to identify plagiarism. In particular, we present(More)
Source code plagiarism can be identified by analyzing similarities of several and diverse aspects of a pair of source code. In this paper we present three types of similarity features that account for three aspects of source code documents, particularly: i) lexical, ii) structural, and iii) stylistics. From the lexical view, we used a character 3-gram model(More)
This paper describes the participation of the Language and Reasoning Group of UAM at RepLab 2013 Profiling evaluation lab. We adopted Distribu-tional Term Representations (DTR) for facing the following problems: i) filtering tweets that are related to an entity, and ii) identifying positive or negative implications for the entity's reputation, i.e.,(More)