McGillivray, Barbara

Nissim, Malvina

Pedrazzini, Nilo

Peels, Saskia

Stopponi, Silvia

English

We evaluate four count-based and predictive distributional semantic models of Ancient Greek against AGREE, a composite benchmark of human judgements, to assess their ability to retrieve semantic relatedness. On the basis of the observations deriving from the analysis of the results, we design a procedure for a larger-scale intrinsic evaluation of count-based and predictive language models, including syntactic embeddings. We also propose possible ways of exploiting the different layers of the whole AGREE benchmark (including both human- and machine-generated data) and different evaluation metrics

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Evaluation of Distributional Semantic Models of Ancient Greek:Preliminary Results and a Road Map for Future Work

https://kclpure.kcl.ac.uk/portal/files/226307043/36.pdf

Evaluation of Distributional Semantic Models of Ancient Greek: Preliminary Results and a Road Map for Future Work

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