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        <title>Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models</title>
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    <abstract>Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations. This paper describes two complementary ways to adapt classifiers to shifts across time. First, we show that diachronic word embeddings, which were originally developed to study language change, can also improve document classification, and we show a simple method for constructing this type of embedding. Second, we propose a time-driven neural classification model inspired by methods for domain adaptation. Experiments on six corpora show how these methods can make classifiers more robust over time.</abstract>
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%0 Conference Proceedings
%T Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
%A Huang, Xiaolei
%A Paul, Michael J.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F huang-paul-2019-neural-temporality
%X Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations. This paper describes two complementary ways to adapt classifiers to shifts across time. First, we show that diachronic word embeddings, which were originally developed to study language change, can also improve document classification, and we show a simple method for constructing this type of embedding. Second, we propose a time-driven neural classification model inspired by methods for domain adaptation. Experiments on six corpora show how these methods can make classifiers more robust over time.
%R 10.18653/v1/P19-1403
%U https://aclanthology.org/P19-1403
%U https://doi.org/10.18653/v1/P19-1403
%P 4113-4123

Markdown (Informal)

[Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models](https://aclanthology.org/P19-1403) (Huang & Paul, ACL 2019)

• Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (Huang & Paul, ACL 2019)

ACL

• Xiaolei Huang and Michael J. Paul. 2019. Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4113–4123, Florence, Italy. Association for Computational Linguistics.