SERC: Syntactic and Semantic Sequence based Event Relation Classification
This work addresses event relation classification for applications like timeline generation and question answering, but it appears incremental as it builds on existing methods with hybrid features.
The paper tackled the problem of classifying temporal and causal relations between events by proposing a joint LSTM-based model that incorporates syntactic and semantic features, achieving promising results on four popular datasets.
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.