Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
复制标题
DOI：
10.18653/v1/p19-1403
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Xiaolei Huang;Michael J. Paul
中科院分区：
文献类型：
--
作者：
Xiaolei Huang;Michael J. Paul
语言的使用可能会随着时间的推移而变化，但文档分类器模型通常是在跨越多年的语料库上进行训练和测试的，而不考虑时间的变化。本文介绍了两种互补的方法来适应分类器的变化，随着时间的推移。首先，我们表明，历时词嵌入，这最初是为了研究语言的变化，也可以提高文档分类，我们展示了一个简单的方法来构建这种类型的嵌入。其次，我们提出了一个时间驱动的神经分类模型的启发域适应的方法。在六个语料库上的实验表明，这些方法可以使分类器随着时间的推移而变得更加鲁棒。
Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations. This paper describes two complementary ways to adapt classifiers to shifts across time. First, we show that diachronic word embeddings, which were originally developed to study language change, can also improve document classification, and we show a simple method for constructing this type of embedding. Second, we propose a time-driven neural classification model inspired by methods for domain adaptation. Experiments on six corpora show how these methods can make classifiers more robust over time.