用语境化的词表征分析词汇语义变化
Analysing Lexical Semantic Change with Contextualised Word Representations
摘要：
本文提出了第一个无监督的方法，利用上下文的词表示的词汇语义变化。我们提出了一种新的方法，利用BERT神经语言模型来获得表示的词的使用，聚类这些表示到使用类型，并测量随着时间的变化与三个建议的指标。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变化与人类的判断呈正相关。我们大量的定性分析表明，我们的方法捕捉到了各种共时和历时的语言现象。我们希望我们的工作能够激发这方面的进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
