TextTransfer – Corpus-based detection of secondary use of scientific publications, 2017-2019
In this collaborative project, we are using Natural Language Processing and Machine Learning to identify secondary practical uses of research findings from final reports of grant funded work. Such reports are often stored in specialized databases, where long-term archiving activities focus on standardization, interoperability, and information indexing and retrieval. However, secondary use of reports is often not enabled or enforced, limiting the replication and reusability of research. We are identifying practically relevant patterns from text data by using information extraction techniques and detect transferable knowledge (from basic research to applications) in selected domains.
Funder: Federal Ministry of Education and Research (Germany) and Institute for German Language (IDS) (Germany)
Biases in Data and Technology, and Data Quality and Provenance
Alliance for IoBT Research on Evolving Intelligent Goal-driven Networks (IoBT REIGN), 2017-2022
Jana Diesner, assistant professor and PhD program director at the iSchool, is co-principal investigator on a multi-institutional initiative funded by the Army Research Lab to enable new predictive battlefield analytics and services. Diesner will collaborate on the project with researchers from Computer Science and Electrical and Computer Engineering.....[Learn More] (Project description courtesy of U of I Coordinated Science Lab)
Funder: Army Research Lab Cooperative Agreement W911NF-17-2-0196
Review and Assessment of the Usage of Computational Methods for Humanitarian Assistance and Disaster Relief (HADR) Efforts, and Scalable Measurement of Emergency Response from Text Data, 2017-2018
The Humanitarian Assistance and Disaster Relief (HADR) project has endeavored to use Natural Language Processing (NLP) to increase situational awareness of humanitarian relief operations. We based our efforts on prior work in and across a number of fields heavily engaged in similar projects. These fields include crisis informatics, humanitarian response, and information systems for crisis response and management respectively. Our product-oriented approach augments existing efforts by comparing different sources of text-based communication to an established ground truth or what is already known to be happening on the ground. By comparing these disparate text-based sources to what we already know, new items can be added as they appear. Our current efforts surround what sources of information represent the most reliably useful information to expand situational awareness for everyone involved in the disrupted region.
Funder: Department of Homeland Security and Critical Infrastructure Resilience Institute
Data Governance and Pratical Ethics
Regulations for Human-Centered Data Science, 2017-present
Data Science projects often involve the collection and analysis of online and open data. These data are governed by multiple sets of norms and regulations. Problems can arise when researchers are unaware of applicable rules, uninformed about their practical meaning, and insufficiently skilled in implementing them. To address this issue, we have have developed and provide education, and conduct research on NLP for data regulations.
Funder: Office of the CIO/ Technology Services at UIUC
ConText: A solution to support text and network analysis.
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