GASC : Genre-Aware Semantic Change for Ancient Greek

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Perrone , V , Palma , M , Hengchen , S , Vatri , A , Smith , J Q & McGillivray , B 2019 , GASC : Genre-Aware Semantic Change for Ancient Greek . in The 1st International Workshop on Computational Approaches to Historical Language Change : Proceedings of the Workshop . ACL , Stroudsburg , pp. 56-66 , International Workshop on Computational Approaches to Historical Language Change , Florence , Italy , 02/08/2019 . https://doi.org/10.18653/v1/w19-4707

Title: GASC : Genre-Aware Semantic Change for Ancient Greek
Author: Perrone, Valerio; Palma, Marco; Hengchen, Simon; Vatri, Alessandro; Smith, Jim Q.; McGillivray, Barbara
Other contributor: University of Helsinki, Digital Humanities

Publisher: ACL
Date: 2019-07
Language: eng
Number of pages: 11
Belongs to series: The 1st International Workshop on Computational Approaches to Historical Language Change Proceedings of the Workshop
ISBN: 978-1-950737-31-4
DOI: https://doi.org/10.18653/v1/w19-4707
URI: http://hdl.handle.net/10138/304515
Abstract: Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.
Subject: 112 Statistics and probability
6121 Languages
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