# A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events

arXiv: https://arxiv.org/abs/1707.07343 · HTML: https://mlsanity.com/paper/1707.07343/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2017.

## Built on by (most cited first, 11 of 11)

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- [Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement](https://mlsanity.com/paper/2106.01654/) (2021)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [Semantic Structure Enhanced Event Causality Identification](https://mlsanity.com/paper/2305.12792/) (2023)
- [More than Classification: A Unified Framework for Event Temporal Relation Extraction](https://mlsanity.com/paper/2305.17607/) (2023)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024)
- [Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs](https://mlsanity.com/paper/2210.11787/) (2022)
- [Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network](https://mlsanity.com/paper/2409.13621/) (2024)
- [SERC: Syntactic and Semantic Sequence based Event Relation Classification](https://mlsanity.com/paper/2111.02265/) (2021)
- [Classifying Dominant Temporal Orientation without Pretrained Text Embeddings: A Novel Morphosyntactic Inventory Vector Approach](https://mlsanity.com/paper/2609.33121/) (2026)

## Abstract (arXiv)

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.