4 accepted papers
As an essential task in information extraction (IE), Event-Event Causal Relation Extraction (ECRE) aims to identify and classify the causal relationships between event mentions in natural language texts. However, existing research on ECRE has highlighted two critical challenges, including the lack o…
Existing benchmarks for multi-hop question answering (QA) primarily evaluate models based on their ability to reason about entities and the relationships between them. However, there's a lack of insight into how these models perform in terms of both events and entities. In this paper, we introduce a…
Monocular self-supervised depth estimation with a low-cost sensor is the mainstream solution to gathering dense depth maps for robots and autonomous driving. In this paper, based on the philosophy "less is more" (i.e., focusing only on valid pixels in sparse LiDAR), we propose a novel framework, Eff…
This study aims to employ deep learning for the development of an automatic classifier for the severity of chronic obstructive pulmonary disease (COPD) in patients. A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to generate a model for classificatio…