DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings
24658Authors
nan
Abstract
AbstractThis paper describes DiaSense, a system developed for Task 1 ‘Unsupervised Lexical Semantic Change Detection’ of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.
Keywords
Full Content
nan
Labeled Text
AbstractThis paper describes DiaSense, a system developed for Task 1 ‘Unsupervised Lexical Semantic Change Detection’ of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.
Publication Details
- Paper ID:
- 24658
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan