Natural Language Processing for Ancient Greek

Author:

Stopponi Silvia1ORCID,Pedrazzini Nilo2ORCID,Peels-Matthey Saskia1,McGillivray Barbara3ORCID,Nissim Malvina1

Affiliation:

1. University of Groningen

2. The Alan Turing Institute

3. King’s College London

Abstract

Abstract Computational methods have produced meaningful and usable results to study word semantics, including semantic change. These methods, belonging to the field of Natural Language Processing, have recently been applied to ancient languages; in particular, language modelling has been applied to Ancient Greek, the language on which we focus. In this contribution we explain how vector representations can be computed from word co-occurrences in a corpus and can be used to locate words in a semantic space, and what kind of semantic information can be extracted from language models. We compare three different kinds of language models that can be used to study Ancient Greek semantics: a count-based model, a word embedding model and a syntactic embedding model; and we show examples of how the quality of their representations can be assessed. We highlight the advantages and potential of these methods, especially for the study of semantic change, together with their limitations.

Publisher

John Benjamins Publishing Company

Reference31 articles.

1. Graph-based Syntactic Word Embeddings

2. The Ancient Greek and Latin Dependency Treebanks

3. Compass-aligned distributional embeddings for studying semantic differences across corpora;Bianchi,2020

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