@inproceedings{chen-etal-2020-ferryman,
title = "Ferryman at {S}em{E}val-2020 Task 3: Bert with {TFIDF}-Weighting for Predicting the Effect of Context in Word Similarity",
author = "Chen, Weilong and
Yuan, Xin and
Zhang, Sai and
Wu, Jiehui and
Zhang, Yanru and
Wang, Yan",
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-1.35",
doi = "10.18653/v1/2020.semeval-1.35",
pages = "281--285",
abstract = "Word similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a way to handle complex features in word usage (such as sentence syntax and semantics), but also an important method for different word polysemy modeling. In this paper, we present the methodology proposed by team Ferryman. Our system is based on the Bidirectional Encoder Representations from Transformers (BERT) model combined with term frequency-inverse document frequency (TF-IDF), applying the method on the provided datasets called CoSimLex, which covers four different languages including English, Croatian, Slovene, and Finnish. Our team Ferryman wins the the first position for English task and the second position for Finnish in the subtask 1.",
}
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%0 Conference Proceedings
%T Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity
%A Chen, Weilong
%A Yuan, Xin
%A Zhang, Sai
%A Wu, Jiehui
%A Zhang, Yanru
%A Wang, Yan
%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 chen-etal-2020-ferryman
%X Word similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a way to handle complex features in word usage (such as sentence syntax and semantics), but also an important method for different word polysemy modeling. In this paper, we present the methodology proposed by team Ferryman. Our system is based on the Bidirectional Encoder Representations from Transformers (BERT) model combined with term frequency-inverse document frequency (TF-IDF), applying the method on the provided datasets called CoSimLex, which covers four different languages including English, Croatian, Slovene, and Finnish. Our team Ferryman wins the the first position for English task and the second position for Finnish in the subtask 1.
%R 10.18653/v1/2020.semeval-1.35
%U https://aclanthology.org/2020.semeval-1.35
%U https://doi.org/10.18653/v1/2020.semeval-1.35
%P 281-285
Markdown (Informal)
[Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity](https://aclanthology.org/2020.semeval-1.35) (Chen et al., SemEval 2020)
ACL