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- W2776562531 abstract "Author profiling consists in determining some demographic attributes — such as gender, age, nationality, language, religion, and others — of an author for a given document. This task, which has applications in fields such as forensics, security, or marketing, has been approached from different areas, especially from linguistics and natural language processing, by extracting different types of features from training documents, usually content — and style-based features. In this paper we address the problem by using several compression-inspired strategies that generate different models without analyzing or extracting specific features from the textual content, making them style-oblivious approaches. We analyze the behavior of these techniques, combine them and compare them with other state-of-the-art methods. We show that they can be competitive in terms of accuracy, giving the best predictions for some domains, and they are efficient in time performance." @default.
- W2776562531 created "2018-01-05" @default.
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- W2776562531 date "2017-12-01" @default.
- W2776562531 modified "2023-09-23" @default.
- W2776562531 title "Competitive Author Profiling Using Compression-Based Strategies" @default.
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- W2776562531 doi "https://doi.org/10.1142/s0218488517400086" @default.
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