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.- Anthology ID:
- 2020.semeval-1.11
- 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:
- 105–111
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.11
- DOI:
- 10.18653/v1/2020.semeval-1.11
- Cite (ACL):
- Maurício Gruppi, Sibel Adali, and Pin-Yu Chen. 2020. SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 105–111, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change (Gruppi et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.semeval-1.11.pdf
- Code
- mgruppi/schme