Analysing Lexical Semantic Change with Contextualised Word Representations

• URL: http://arxiv.org/abs/2004.14118v1
• Date: Wed, 29 Apr 2020 12:18:14 GMT
• Title: Analysing Lexical Semantic Change with Contextualised Word Representations
• Authors: Mario Giulianelli, Marco Del Tredici, Raquel Fern\'andez
• Abstract summary: We propose a novel method that exploits the BERT neural language model to obtain representations of word usages. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.
• Score: 7.071298726856781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements. Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena. We expect our work to inspire further research in this direction.

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