Neural Temporality Adaptation for Document Classification:
Diachronic Word Embeddings and Domain Adaptation Models
Abstract
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