Word Embeddings: Reliability & Semantic Change

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Author
Hellrich, J.
Pub. date
August 2019
Pages
188
Binding
softcover
Volume
347 of Dissertations in Artificial Intelligence
ISBN print
978-1-61499-994-2
ISBN online
978-1-61499-995-9
Subject
Artificial Intelligence, Computer & Communication Sciences
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Word embeddings are a form of distributional semantics increasingly popular for investigating lexical semantic change. However, typical training algorithms are probabilistic, limiting their reliability and the reproducibility of studies.


Johannes Hellrich investigated this problem both empirically and theoretically and found some variants of SVD-based algorithms to be unaffected. Furthermore, he created the JeSemE website to make word embedding based diachronic research more accessible. It provides information on changes in word denotation and emotional connotation in five diachronic corpora.


Finally, the author conducted two case studies on the applicability of these methods by investigating the historical understanding of electricity as well as words connected to Romanticism. They showed the high potential of distributional semantics for further applications in the digital humanities.