Interpreting ancient languages involves guesswork about semantics, as well as polysemy (the coexistence of many possible meanings for a word or phrase), and context.
Researchers at the Alan Turing Institute, the University of Warwick, the University of Helsinki, and Amazon propose a novel solution in a newly published paper. The idea involves neural networks, or layered mathematical functions that model biological neurons. Dubbed Genre-Aware Semantic Change for Ancient Greek (GASC), it leverages categorical metadata about target texts’ genres to uncover the evolution of meanings in Ancient Greek data sets….
The team’s work goes beyond literary data sets and historical language data and directly addresses questions about genre — i.e., which genre is most likely associated with a given sense, what is an unusual sense for a genre, and which genres have the most similar senses….
“In technical texts, we expect polysemous words to have a technical sense,” the team explains. “On the other hand, in works more closely representing general language (comedy, oratory, historiography) we expect the words to appear in their more concrete and less metaphorical senses; in a number of genres, such as philosophy and tragedy, we cannot assume that this distribution holds.”…
In experiments, the researchers report that GASC was able to provide “interpretable representations” of the evolution of word sense and that it achieves improved predictive performance compared to the state of the art.
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