Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity
Weilong Chen, Xin Yuan, Sai Zhang, Jiehui Wu, Yanru Zhang, Yan Wang
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.- Anthology ID:
- 2020.semeval-1.35
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 281–285
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.35
- DOI:
- 10.18653/v1/2020.semeval-1.35
- Cite (ACL):
- Weilong Chen, Xin Yuan, Sai Zhang, Jiehui Wu, Yanru Zhang, and Yan Wang. 2020. Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 281–285, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity (Chen et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.semeval-1.35.pdf