A Bayesian Model of Diachronic Meaning Change
A Bayesian Model of Diachronic Meaning Change
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DOI：
10.1162/tacl_a_00081
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发表时间：
2016-02
影响因子：
10.9
通讯作者：
Lea Frermann;Mirella Lapata
中科院分区：
文献类型：
--
作者：
Lea Frermann;Mirella Lapata
词义随着时间的推移而变化，从文本中提取这些信息的自动程序将对历史探索性研究、信息检索或问题回答非常有用。我们提出了一个历时意义变化的动态贝叶斯模型，该模型推断了时间词表征作为一组意义及其流行程度。与以前的工作不同，我们明确地将语言变化建模为一个平稳、渐进的过程。我们的实验表明，这种建模决策是有益的：我们的模型在诱导可识别的词义及其随时间发展的同时，在意义变化检测任务上表现得很有竞争力。将我们的模型应用于SemEval-2015时间分类基准数据集进一步表明，它的性能与高度优化的任务特定系统相当。
Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.