GASC: Genre-Aware Semantic Change for Ancient Greek

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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Kuvaus

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.
Alkuperäiskielienglanti
OtsikkoThe 1st InternationalWorkshop on Computational Approaches to Historical Language Change : Proceedings of the Workshop
Sivumäärä11
JulkaisupaikkaStroudsburg
KustantajaACL
Julkaisupäiväheinäkuuta 2019
Sivut56-66
ISBN (elektroninen)978-1-950737-31-4
TilaJulkaistu - heinäkuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Workshop on Computational
Approaches to Historical Language Change
- Florence, Italia
Kesto: 2 elokuuta 20192 elokuuta 2019
Konferenssinumero: 1

Tieteenalat

  • 112 Tilastotiede
  • 6121 Kielitieteet

Lainaa tätä

Perrone, V., Palma, M., Hengchen, S., Vatri, A., Smith, J. Q., & McGillivray, B. (2019). GASC: Genre-Aware Semantic Change for Ancient Greek. teoksessa The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop (Sivut 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. Sivut 56-66
@inproceedings{128e4b72c0284a51a59ba6a6db3ae9a5,
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.",
keywords = "112 Statistics and probability, 6121 Languages",
author = "Valerio Perrone and Marco Palma and Simon Hengchen and Alessandro Vatri and Smith, {Jim Q.} and Barbara McGillivray",
year = "2019",
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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. julkaisussa The 1st InternationalWorkshop on Computational Approaches to Historical Language Change: Proceedings of the Workshop. ACL, Stroudsburg, Sivut 56-66, International Workshop on Computational
Approaches to Historical Language Change, Florence, Italia, 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.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

TY - GEN

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

PY - 2019/7

Y1 - 2019/7

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.

KW - 112 Statistics and probability

KW - 6121 Languages

M3 - Conference contribution

SP - 56

EP - 66

BT - The 1st InternationalWorkshop on Computational Approaches to Historical Language Change

PB - ACL

CY - Stroudsburg

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