Ning Qiang

Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer.

2021 · arXiv (Cornell University)

Extracting temporal relations (e.g., before, after, concurrent) among events is crucial to natural language understanding. Previous studies mainly rely on neural networks to learn effective features or manual-crafted linguistic features for temporal relation extraction, which …

A Structured Learning Approach to Temporal Relation Extraction

2017

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, …

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

2019

Rujun Han, Qiang Ning, Nanyun Peng. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Temporal Common Sense Acquisition with Minimal Supervision

2020

Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on …