Qiang Ning

This paper proposes a new multi-axis modeling to better capture the temporal structure of events. Expand

In this paper, we propose a structured learning approach to temporal relation extraction, where local models are updated based on feedback from global inferences. Expand

Understanding temporal and causal relations between events is a fundamental natural language understanding task. Expand

We define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. Expand

This paper develops such a resource -- a probabilistic knowledge base acquired in the news domain -- by extracting temporal relations between events from the New York Times (NYT) articles. Expand

We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. Expand

We present CogCompTime, a system that has these two important functionalities. Expand

To improve signal‐to‐noise ratio (SNR) for high‐resolution spectroscopic imaging using a subspace‐based technique known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE). Expand

We introduce tensor decomposition as a generalization of matrix decomposition and also its unique properties that make it different to matrices. Expand

We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. Expand