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akaturk Akademik ölçüm

OpenAlex konusu

Authorship Attribution and Profiling

Bu sayfa OpenAlex konu etiketine göre çalışmaları ve o konuda görünen akademisyenleri listeler. YÖKSİS temel alan / yan dal değildir.

OpenAlex 276 eser 18 yazar konusu

Çalışmalar

276 eser

  1. YÖKSİS SJR Q1 JCR Q2 OpenAlex üst %10 OpenAlex 96.2%

    Text genre classification is the process of identifying functional characteristics of text documents. The immense quantity of text documents available on the web can be properly filtered, organised and retrieved with the use of text genre classification, which may have potential use on several other tasks of natural l…

  2. YÖKSİS SJR Q2 JCR Q3 OpenAlex üst %10 OpenAlex 97.5%

    Özet henüz yok.

  3. YÖKSİS SJR Q2 JCR Q3 OpenAlex üst %10 OpenAlex 97.5%

    Özet henüz yok.

  4. OpenAlex üst %10 OpenAlex 98.7%

    The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, pose interesting challenges in this respect. This article presents an invest…

  5. OpenAlex 72.1%

    Özet henüz yok.

  6. YÖKSİS OpenAlex üst %10 OpenAlex 95.3%

    Spearphishing is a prominent targeted attack vector in today's Internet. By impersonating trusted email senders through carefully crafted messages and spoofed metadata, adversaries can trick victims into launching attachments containing malicious code or into clicking on malicious links that grant attackers a foothold…

  7. OpenAlex üst %10 OpenAlex 96.6%

    This paper explores the morphosyntactic tools for text watermarking and develops a syntax-based natural language watermarking scheme. Turkish, an agglutinative language, provides a good ground for the syntax-based natural language watermarking with its relatively free word order possibilities and rich repertoire of mo…

  8. YÖKSİS SJR Q2 JCR Q2 OpenAlex üst %10 OpenAlex 96.0%

    Özet henüz yok.

  9. YÖKSİS SJR Q2 JCR Q2 OpenAlex üst %10 OpenAlex 96.0%

    Özet henüz yok.

  10. OpenAlex üst %10 OpenAlex 95.6%

    We present an approach to identifying Twitter paraphrases using simple lexical over-lap features. The work is part of ongoing re-search into the applicability of knowledge-lean techniques to paraphrase identification. We utilize features based on overlap of word and character n-grams and train support vector machine (…

  11. OpenAlex üst %10 OpenAlex 95.6%

    Özet henüz yok.

  12. OpenAlex 5.0%

    Özet henüz yok.

Akademisyenler

18 akademisyen