@inproceedings{rosin-radinsky-2019-generating,
title = "Generating Timelines by Modeling Semantic Change",
author = "Rosin, Guy D. and
Radinsky, Kira",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1018",
doi = "10.18653/v1/K19-1018",
pages = "186--195",
abstract = "Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical {``}turning points{''}, represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.",
}
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<abstract>Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical “turning points”, represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.</abstract>
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%0 Conference Proceedings
%T Generating Timelines by Modeling Semantic Change
%A Rosin, Guy D.
%A Radinsky, Kira
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F rosin-radinsky-2019-generating
%X Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical “turning points”, represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.
%R 10.18653/v1/K19-1018
%U https://aclanthology.org/K19-1018
%U https://doi.org/10.18653/v1/K19-1018
%P 186-195
Markdown (Informal)
[Generating Timelines by Modeling Semantic Change](https://aclanthology.org/K19-1018) (Rosin & Radinsky, CoNLL 2019)
ACL
- Guy D. Rosin and Kira Radinsky. 2019. Generating Timelines by Modeling Semantic Change. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 186–195, Hong Kong, China. Association for Computational Linguistics.