论文标题
用上下文化的单词表示分析词汇语义变化
Analysing Lexical Semantic Change with Contextualised Word Representations
论文作者
论文摘要
本文提出了使用上下文化的单词表示的第一种无监督语义变化的方法。我们提出了一种新的方法，该方法利用了伯特神经语言模型以获取单词用法的表示形式，将这些表示形式簇为用法类型，并通过三个建议的指标随时间而变化。我们创建了一个新的评估数据集，并表明模型表示和检测到的语义转移与人类判断呈正相关。我们广泛的定性分析表明，我们的方法捕获了各种同步和历时性语言现象。我们希望我们的工作能够激发这一方向的进一步研究。
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.
