  Temporal and causal relations play an important role in determining the
dependencies between events. Classifying the temporal and causal relations
between events has many applications, such as generating event timelines, event
summarization, textual entailment and question answering. Temporal and causal
relations are closely related and influence each other. So we propose a joint
model that incorporates both temporal and causal features to perform causal
relation classification. We use the syntactic structure of the text for
identifying temporal and causal relations between two events from the text. We
extract parts-of-speech tag sequence, dependency tag sequence and word sequence
from the text. We propose an LSTM based model for temporal and causal relation
classification that captures the interrelations between the three encoded
features. Evaluation of our model on four popular datasets yields promising
results for temporal and causal relation classification.
