Schneider and students discuss framework for information retrieval at ECIR 2018

Jodi Schneider
Jodi Schneider, Associate Professor

Assistant Professor Jodi Schneider, CAS student Janina Sarol (MSIM '17), and undergraduate Linxi Liu will discuss their research at the European Conference on Information Retrieval (ECIR 2018) in Grenoble, France. Sarol will present their paper, "Testing a Citation and Text-Based Framework for Retrieving Publications for Literature Reviews," at the conference’s Bibliometric-enhanced Information Retrieval workshop on March 26.

Using the framework they created, the researchers collected articles that were connected in the citation network and filtered them using a combination of citation- and text-based criteria. Their paper discusses how well their framework performed in its first implementation, compared to conventional search methods of six published systematic reviews.

"Using different combinations of seed articles, we were able to retrieve up to eighty-seven percent of the total included studies in the published reviews and one hundred percent of the studies available in the search database we mined," said Schneider. "In the worst case, we retrieved five percent more results than the conventional search methods. These results suggest that our framework is a promising complementary approach to help reduce the number of articles manually screened by reviewers."

Schneider studies scholarly communication and social media through the lens of arguments, evidence, and persuasion. She is developing linked data (ontologies, metadata, Semantic Web) approaches to manage scientific evidence. She holds a PhD in informatics from the National University of Ireland, Galway. Prior to joining the iSchool in 2016, Schneider served as a postdoctoral scholar at the National Library of Medicine, University of Pittsburgh, and INRIA, the national French Computer Science Research Institute. 

Updated on
Backto the news archive

Related News

Trainor receives the Karen Wold Level the Learning Field Award

Senior Lecturer Kevin Trainor has been selected by the Division of Disability Resources and Educational Services (DRES) to receive the 2024 Karen Wold Level the Learning Field Award. This award honors exemplary members of faculty and staff for advocating and/or implementing instructional strategies, technologies, and disability-related accommodations that afford students with disabilities equal access to academic resources and curricula. 

Kevin Trainor

Seo coauthors chapter on data science and accessibility

Assistant Professor JooYoung Seo and Mine Dogucu, professor of statistics in the Donald Bren School of Information and Computer Sciences at the University of California Irvine, have coauthored a chapter in the new book Teaching Accessible Computing. The goal of the book, which is edited by Alannah Oleson, Amy J. Ko and Richard Ladner, is to help educators feel confident in introducing topics related to disability and accessible computing and integrating accessibility into their courses.

JooYoung Seo

iSchool instructors ranked as excellent

Fifty-five iSchool instructors were named in the University's List of Teachers Ranked as Excellent for Fall 2023. The rankings are released every semester, and results are based on the Instructor and Course Evaluation System (ICES) questionnaire forms maintained by Measurement and Evaluation in the Center for Innovation in Teaching and Learning. 

iSchool Building

ConnectED: Tech for All podcast launched by Community Data Clinic

The Community Data Clinic (CDC), a mixed methods data studies and interdisciplinary community research lab led by Associate Professor Anita Say Chan, has released the first episode of its new podcast, ConnectED: Tech for All. Community partners on the podcast include the Housing Authority of Champaign County, Champaign-Urbana Public Health District, Project Success of Vermilion County, and Cunningham Township Supervisor’s Office.

Community Data Clinic podcast logo

New study shows LLMs respond differently based on user’s motivation

A new study conducted by PhD student Michelle Bak and Assistant Professor Jessie Chin, which was recently published in the Journal of the American Medical Informatics Association (JAMIA), reveals how large language models (LLMs) respond to different motivational states. In their evaluation of three LLM-based generative conversational agents (GAs)—ChatGPT, Google Bard, and Llama 2—the researchers found that while GAs are able to identify users' motivation states and provide relevant information when individuals have established goals, they are less likely to provide guidance when the users are hesitant or ambivalent about changing their behavior.