@inproceedings{nulty-lillis-2020-ucd,
title = "The {UCD}-Net System at {S}em{E}val-2020 Task 1: Temporal Referencing with Semantic Network Distances",
author = "Nulty, Paul and
Lillis, David",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.13",
doi = "10.18653/v1/2020.semeval-1.13",
pages = "119--125",
abstract = "This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an implementation of a web application for the purpose of searching and visualising semantic networks. The results of the change measure used for this task were not among the best performing systems, but further calibration of the distance metric and backoff approaches may improve this method.",
}
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<abstract>This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an implementation of a web application for the purpose of searching and visualising semantic networks. The results of the change measure used for this task were not among the best performing systems, but further calibration of the distance metric and backoff approaches may improve this method.</abstract>
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%0 Conference Proceedings
%T The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances
%A Nulty, Paul
%A Lillis, David
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F nulty-lillis-2020-ucd
%X This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an implementation of a web application for the purpose of searching and visualising semantic networks. The results of the change measure used for this task were not among the best performing systems, but further calibration of the distance metric and backoff approaches may improve this method.
%R 10.18653/v1/2020.semeval-1.13
%U https://aclanthology.org/2020.semeval-1.13
%U https://doi.org/10.18653/v1/2020.semeval-1.13
%P 119-125
Markdown (Informal)
[The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances](https://aclanthology.org/2020.semeval-1.13) (Nulty & Lillis, SemEval 2020)
ACL