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. In short%2C generated definitions are used as `senses'%2C and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages%2C we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time%2C it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.?quality=80&w=800)
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