@inproceedings{zamora-reina-bravo-marquez-2020-dcc,
title = "{DCC}-Uchile at {S}em{E}val-2020 Task 1: Temporal Referencing Word Embeddings",
author = "Zamora-Reina, Frank D. and
Bravo-Marquez, Felipe",
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-1.23",
doi = "10.18653/v1/2020.semeval-1.23",
pages = "194--200",
abstract = "We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another. Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time. This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens. Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity. Our submission was ranked 7th among 34 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.",
}
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<abstract>We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another. Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time. This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens. Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity. Our submission was ranked 7th among 34 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.</abstract>
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%0 Conference Proceedings
%T DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings
%A Zamora-Reina, Frank D.
%A Bravo-Marquez, Felipe
%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 zamora-reina-bravo-marquez-2020-dcc
%X We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another. Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time. This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens. Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity. Our submission was ranked 7th among 34 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.
%R 10.18653/v1/2020.semeval-1.23
%U https://aclanthology.org/2020.semeval-1.23
%U https://doi.org/10.18653/v1/2020.semeval-1.23
%P 194-200
Markdown (Informal)
[DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings](https://aclanthology.org/2020.semeval-1.23) (Zamora-Reina & Bravo-Marquez, SemEval 2020)
ACL