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Februar 2024</time><time class="updated" datetime="2024-02-02T10:40:32+01:00">2. Februar 2024</time></a></div><h2 class="entry-title"><a href="https://dhistory.hypotheses.org/6656" rel="bookmark">Thea Sommerschield: Machine Learning for Ancient Languages. Trends, challenges, and future prospects for a digital ancient history</a></h2> </header> <div class="post-thumbnail"> <a href="https://dhistory.hypotheses.org/6656"> <img width="1060" height="380" src="https://dhistory.hypotheses.org/files/2024/02/Fig.2_SommerschieldNature.webp" class="attachment-twentyseventeen-featured-image size-twentyseventeen-featured-image wp-post-image" alt="" decoding="async" srcset="https://dhistory.hypotheses.org/files/2024/02/Fig.2_SommerschieldNature.webp 1060w, https://dhistory.hypotheses.org/files/2024/02/Fig.2_SommerschieldNature-300x108.webp 300w, https://dhistory.hypotheses.org/files/2024/02/Fig.2_SommerschieldNature-500x179.webp 500w, https://dhistory.hypotheses.org/files/2024/02/Fig.2_SommerschieldNature-768x275.webp 768w" sizes="(max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px" /> </a> </div> <div class="entry-content"> <p>Recent advances in Artificial Intelligence and Machine Learning (ML) have facilitated unprecedented analyses of ancient languages with unparalleled detail. ML methods now enable the exploration of several tasks associated with the study of ancient texts, including decipherment, digitization, attribution, studies in intertextuality and semantics.</p> <a href="https://dhistory.hypotheses.org/6656#more-6656" class="more-link"><span class="screen-reader-text">„Thea Sommerschield: Machine Learning for Ancient Languages. 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