Trends in AI Research for the Visual Surveillance of Populations

Acharya, Ashwin; Langenkamp, Max; Dunham, James

doi:10.51593/20200097

Cited by 2 publications

(1 citation statement)

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“…These knowledge graphs can be used to discover clusters of papers belonging to topics of research. Recently, researchers have been attempting to automatically classify documents [11] and discover clusters of paper related to a specific task [4]. Moreover, such knowledge graphs are already in use helping policy makers look into research funding practices in artificial intelligence [22].…”

Section: Introductionmentioning

confidence: 99%

Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions

Yousuf¹,

Biswas²,

Kaushal³

et al. 2022

Preprint

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Understanding key insights from full-text scholarly articles is essential as it enables us to determine interesting trends, give insight into the research and development, and build knowledge graphs. However, some of the interesting key insights are only available when considering full-text. Although researchers have made significant progress in information extraction from short documents, extraction of scientific entities from full-text scholarly literature remains a challenging problem. This work presents an automated End-toend Research Entity Extractor called EneRex to extract technical facets such as dataset usage, objective task, method from full-text scholarly research articles. Additionally, we extracted three novel facets, e.g., links to source code, computing resources, programming language/libraries from full-text articles. We demonstrate how EneRex is able to extract key insights and trends from a largescale dataset in the domain of computer science. We further test our pipeline on multiple datasets and found that the EneRex improves upon a state of the art model. We highlight how the existing datasets are limited in their capacity and how EneRex may fit into an existing knowledge graph. We also present a detailed discussion with pointers for future research. Our code and data are publicly available at https://github.com/DiscoveryAnalyticsCenter/EneRex .

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Section: Introductionmentioning

confidence: 99%