GASC: Genre-Aware Semantic Change for Ancient Greek

Valerio Perrone, Marco Palma, Simon Hengchen, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationThe 1st InternationalWorkshop on Computational Approaches to Historical Language Change : Proceedings of the Workshop
Antal sidor11
UtgivningsortStroudsburg
FörlagACL
Utgivningsdatumjul 2019
Sidor56-66
ISBN (elektroniskt)978-1-950737-31-4
StatusPublicerad - jul 2019
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational Workshop on Computational
Approaches to Historical Language Change
- Florence, Italien
Varaktighet: 2 aug 20192 aug 2019
Konferensnummer: 1

Vetenskapsgrenar

  • 112 Statistik
  • 6121 Språkvetenskaper

Citera det här

Perrone, V., Palma, M., Hengchen, S., Vatri, A., Smith, J. Q., & McGillivray, B. (2019). GASC: Genre-Aware Semantic Change for Ancient Greek. I The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop (s. 56-66). Stroudsburg: ACL.
Perrone, Valerio ; Palma, Marco ; Hengchen, Simon ; Vatri, Alessandro ; Smith, Jim Q. ; McGillivray, Barbara. / GASC: Genre-Aware Semantic Change for Ancient Greek. The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop. Stroudsburg : ACL, 2019. s. 56-66
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title = "GASC: Genre-Aware Semantic Change for Ancient Greek",
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.",
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Perrone, V, Palma, M, Hengchen, S, Vatri, A, Smith, JQ & McGillivray, B 2019, GASC: Genre-Aware Semantic Change for Ancient Greek. i The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop. ACL, Stroudsburg, s. 56-66, International Workshop on Computational
Approaches to Historical Language Change, Florence, Italien, 02/08/2019.

GASC: Genre-Aware Semantic Change for Ancient Greek. / Perrone, Valerio; Palma, Marco; Hengchen, Simon; Vatri, Alessandro; Smith, Jim Q.; McGillivray, Barbara.

The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop. Stroudsburg : ACL, 2019. s. 56-66.

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

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T1 - GASC: Genre-Aware Semantic Change for Ancient Greek

AU - Perrone, Valerio

AU - Palma, Marco

AU - Hengchen, Simon

AU - Vatri, Alessandro

AU - Smith, Jim Q.

AU - McGillivray, Barbara

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N2 - 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.

AB - 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.

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Perrone V, Palma M, Hengchen S, Vatri A, Smith JQ, McGillivray B. GASC: Genre-Aware Semantic Change for Ancient Greek. I The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop. Stroudsburg: ACL. 2019. s. 56-66