  We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our approach first extracts a sequence of context words that
indicates the temporal relation between two events, which well align with the
dependency path between two event mentions. The context word sequence, together
with a parts-of-speech tag sequence and a dependency relation sequence that are
generated corresponding to the word sequence, are then provided as input to
bidirectional recurrent neural network (LSTM) models. The neural nets learn
compositional syntactic and semantic representations of contexts surrounding
the two events and predict the temporal relation between them. Evaluation of
the proposed approach on TimeBank corpus shows that sequential modeling is
capable of accurately recognizing temporal relations between events, which
outperforms a neural net model using various discrete features as input that
imitates previous feature based models.
