A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models

Xingmeng, Zhao,; Niazi‎, Ali; Rios, Anthony

doi:10.48550/arxiv.2212.12799

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“…For example, in the report by Mishra et al [26], some NER models are better at identifying White names across all datasets with higher confidence compared with other demographics, such as Black names. In addition, Zhao et al [39] found that some NER systems are prone to identifying female names as chemicals, and most NER systems perform better on male-related data than female-related data.…”

Section: Introductionmentioning

confidence: 99%

The Shenzhen-Hong Kong Dialectics

Hu¹

2020

The Shenzhen Phenomenon

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Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand out to provide unprecedented immersive and interactive video watching experience. Despite the tremendous potential, the research towards 3D volumetric video is still in its infancy, relying on sufficient and complete datasets for further exploration. However, existing related volumetric video datasets mostly only include a single object, lacking details about the scene and the interaction between them. In this paper, we focus on the current most widely used data format, point cloud, and for the first time release a fullscene volumetric video dataset that includes multiple people and their daily activities interacting with the external environments. Comprehensive dataset description and analysis are conducted, with potential usage of this dataset. The dataset and additional tools can be accessed via the following website: https://cuhkszinml.github.io/full_scene_volumetric_video_dataset/.

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Section: Introductionmentioning

confidence: 99%

The Shenzhen-Hong Kong Dialectics

Hu¹

2020

The Shenzhen Phenomenon

View full text Add to dashboard Cite

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A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models

Cited by 1 publication

References 0 publications

The Shenzhen-Hong Kong Dialectics

The Shenzhen-Hong Kong Dialectics

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