@inproceedings{gruppi-etal-2020-schme,
title = "{SC}h{ME} at {S}em{E}val-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change",
author = "Gruppi, Maur{\'i}cio and
Adali, Sibel and
Chen, Pin-Yu",
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-1.11/",
doi = "10.18653/v1/2020.semeval-1.11",
pages = "105--111",
abstract = "This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME uses a model ensemble combining signals distributional models (word embeddings) and word frequency where each model casts a vote indicating the probability that a word suffered semantic change according to that feature. More specifically, we combine cosine distance of word vectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance (MAP), and a word frequency differential metric as input signals to our model. Additionally, we explore alignment-based methods to investigate the importance of the landmarks used in this process. Our results show evidence that the number of landmarks used for alignment has a direct impact on the predictive performance of the model. Moreover, we show that languages that suffer less semantic change tend to benefit from using a large number of landmarks, whereas languages with more semantic change benefit from a more careful choice of landmark number for alignment."
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<abstract>This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME uses a model ensemble combining signals distributional models (word embeddings) and word frequency where each model casts a vote indicating the probability that a word suffered semantic change according to that feature. More specifically, we combine cosine distance of word vectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance (MAP), and a word frequency differential metric as input signals to our model. Additionally, we explore alignment-based methods to investigate the importance of the landmarks used in this process. Our results show evidence that the number of landmarks used for alignment has a direct impact on the predictive performance of the model. Moreover, we show that languages that suffer less semantic change tend to benefit from using a large number of landmarks, whereas languages with more semantic change benefit from a more careful choice of landmark number for alignment.</abstract>
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%0 Conference Proceedings
%T SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
%A Gruppi, Maurício
%A Adali, Sibel
%A Chen, Pin-Yu
%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 gruppi-etal-2020-schme
%X This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME uses a model ensemble combining signals distributional models (word embeddings) and word frequency where each model casts a vote indicating the probability that a word suffered semantic change according to that feature. More specifically, we combine cosine distance of word vectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance (MAP), and a word frequency differential metric as input signals to our model. Additionally, we explore alignment-based methods to investigate the importance of the landmarks used in this process. Our results show evidence that the number of landmarks used for alignment has a direct impact on the predictive performance of the model. Moreover, we show that languages that suffer less semantic change tend to benefit from using a large number of landmarks, whereas languages with more semantic change benefit from a more careful choice of landmark number for alignment.
%R 10.18653/v1/2020.semeval-1.11
%U https://aclanthology.org/2020.semeval-1.11/
%U https://doi.org/10.18653/v1/2020.semeval-1.11
%P 105-111
Markdown (Informal)
[SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change](https://aclanthology.org/2020.semeval-1.11/) (Gruppi et al., SemEval 2020)
ACL