Detection of Temporal Shifts in Semantics Using Local Graph Clustering
Detection of Temporal Shifts in Semantics Using Local Graph Clustering
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DOI：
10.3390/make5010008
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
N. Hwang;S. Chatterjee;Yanming Di;Sharmodeep Bhattacharyya
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文献类型：
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作者：
N. Hwang;S. Chatterjee;Yanming Di;Sharmodeep Bhattacharyya
我们的数字语料库中的许多变化是由数字通信的快速发展和当前环境之间的相互作用所带来的，这些环境的特点是流行病，政治两极分化和社会动荡。其中一个变化是新词进入大众词汇的速度，以及现有表达的含义、感知和解释发生变化的频率。目前最先进的算法不允许直观和严格的检测这些变化的词义随着时间的推移。我们提出了一个动态的图论方法来推断单词和短语（术语）的语义和检测时间的变化。我们的方法表示每个术语作为一个随机的时间演变的上下文单词集，是一个基于计数的分布语义模型的性质。我们使用局部聚类技术来评估给定单词的上下文单词的结构变化。我们通过调查短语“Chinavirus”的语义变化来证明我们的方法的有效性。我们的结论是，当白宫在2020年3月下半月使用这个词时，这个词的贬义要多得多，尽管这种影响似乎是暂时的。我们提供了用于生成本文结果的数据集和代码。
Many changes in our digital corpus have been brought about by the interplay between rapid advances in digital communication and the current environment characterized by pandemics, political polarization, and social unrest. One such change is the pace with which new words enter the mass vocabulary and the frequency at which meanings, perceptions, and interpretations of existing expressions change. The current state-of-the-art algorithms do not allow for an intuitive and rigorous detection of these changes in word meanings over time. We propose a dynamic graph-theoretic approach to inferring the semantics of words and phrases (“terms”) and detecting temporal shifts. Our approach represents each term as a stochastic time-evolving set of contextual words and is a count-based distributional semantic model in nature. We use local clustering techniques to assess the structural changes in a given word’s contextual words. We demonstrate the efficacy of our method by investigating the changes in the semantics of the phrase “Chinavirus”. We conclude that the term took on a much more pejorative meaning when the White House used the term in the second half of March 2020, although the effect appears to have been temporary. We make both the dataset and the code used to generate this paper’s results available.