  Extracting event time from news articles is a challenging but attractive
task. In contrast to the most existing pair-wised temporal link annotation,
Reimers et al.(2016) proposed to annotate the time anchor (a.k.a. the exact
time) of each event. Their work represents time anchors with discrete
representations of Single-Day/Multi-Day and Certain/Uncertain. This increases
the complexity of modeling the temporal relations between two time anchors,
which cannot be categorized into the relations of Allen's interval algebra
(Allen, 1990).
  In this paper, we propose an effective method to decompose such complex
temporal relations into sub-level relations by introducing a unified quadruple
representation for both Single-Day/Multi-Day and Certain/Uncertain time
anchors. The temporal relation classifiers are trained in a multi-label
classification manner. The system structure of our approach is much simpler
than the existing decision tree model (Reimers et al., 2018), which is composed
by a dozen of node classifiers. Another contribution of this work is to
construct a larger event time corpus (256 news documents) with a reasonable
Inter-Annotator Agreement (IAA), for the purpose of overcoming the data
shortage of the existing event time corpus (36 news documents). The empirical
results show our approach outperforms the state-of-the-art decision tree model
and the increase of data size obtained a significant improvement of
performance.
