2019
ACL
ACL 2019
Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry
Abstract
AbstractDue to its semantic succinctness and novelty of expression, poetry is a great test-bed for semantic change analysis. However, so far there is a scarcity of large diachronic corpora. Here, we provide a large corpus of German poetry which consists of about 75k poems with more than 11 million tokens, with poems ranging from the 16th to early 20th century. We then track semantic change in this corpus by investigating the rise of tropes (‘love is magic’) over time and detecting change points of meaning, which we find to occur particularly within the German Romantic period. Additionally, through self-similarity, we reconstruct literary periods and find evidence that the law of linear semantic change also applies to poetry.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— german poetry
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Learning Types > Self-Supervised Learning
Mathematics & Optimization > Mathematics > Statistics
Interdisciplinary > Linguistics > Semantics
Artificial Intelligence > Core AI > Natural Language Processing