Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View

Renfen Hu, Shen Li, Shichen Liang


Abstract
Diachronic word embeddings have been widely used in detecting temporal changes. However, existing methods face the meaning conflation deficiency by representing a word as a single vector at each time period. To address this issue, this paper proposes a sense representation and tracking framework based on deep contextualized embeddings, aiming at answering not only what and when, but also how the word meaning changes. The experiments show that our framework is effective in representing fine-grained word senses, and it brings a significant improvement in word change detection task. Furthermore, we model the word change from an ecological viewpoint, and sketch two interesting sense behaviors in the process of language evolution, i.e. sense competition and sense cooperation.
Anthology ID:
P19-1379
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3899–3908
Language:
URL:
https://aclanthology.org/P19-1379
DOI:
10.18653/v1/P19-1379
Bibkey:
Cite (ACL):
Renfen Hu, Shen Li, and Shichen Liang. 2019. Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3899–3908, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (Hu et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1379.pdf