Diachronic word embeddings and semantic shifts: a survey
Diachronic word embeddings and semantic shifts: a survey
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发表时间：
2018-06
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通讯作者：
Andrey Kutuzov;Lilja Øvrelid;Terrence Szymanski;Erik Velldal
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作者：
Andrey Kutuzov;Lilja Øvrelid;Terrence Szymanski;Erik Velldal
近年来，出现了一个激增的出版物，旨在跟踪时间变化的词汇语义分布的方法，特别是基于预测的词嵌入模型。然而，这种研究缺乏凝聚力，共同的术语和自然语言处理更成熟的领域的共享实践。本文综述了词嵌入和语义转移检测的研究现状。我们首先讨论语义转移的概念，然后继续概述现有的方法来跟踪这种与时间相关的转移与词嵌入模型。我们提出了几个轴沿着，这些方法可以进行比较，并概述了这个新兴的NLP子领域，以及前景和可能的应用面前的主要挑战。
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.