TEAMBEAM GITHUB HOW TO

We introduced the concept of extending the retrieval process by including authorship information to allow for identifying text passages written by a particular author.

TEAMBEAM GITHUB HOW TO

In previous work, we illustrated how to incorporate ideas of authorship attribution into the mentioned areas. Identifying and attributing authorship of different text passages can be beneficial for various tasks and areas including bibliometrics, information retrieval and plagiarism detection.

The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style. While in the second, the evaluators described the process and the features on which their judgment was based. In the first experiment, we compared the decisions against content features and stylometric features. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. Both studies confirmed that this task is quite challenging. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. content-agnostic, characteristics of authors. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. Unlike the majority of the work done in this field (i.e. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. In this work, we focus on analyzing the way humans judge different writing styles. The goal of our work is inspired by the task of associating segments of text to their real authors.

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