Award Abstract # 1749917
CAREER: Learning Multi-Level Narrative Structure

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: FLORIDA INTERNATIONAL UNIVERSITY
Initial Amendment Date: January 17, 2018
Latest Amendment Date: May 23, 2022
Award Number: 1749917
Award Instrument: Continuing Grant
Program Manager: Andy Duan
yduan@nsf.gov
 (703)292-4286
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: June 1, 2018
End Date: May 31, 2025 (Estimated)
Total Intended Award Amount: $550,000.00
Total Awarded Amount to Date: $660,000.00
Funds Obligated to Date: FY 2018 = $122,499.00
FY 2019 = $111,655.00

FY 2020 = $221,494.00

FY 2021 = $100,819.00

FY 2022 = $103,533.00
History of Investigator:
  • Mark Finlayson (Principal Investigator)
    markaf@fiu.edu
Recipient Sponsored Research Office: Florida International University
11200 SW 8TH ST
MIAMI
FL  US  33199-2516
(305)348-2494
Sponsor Congressional District: 26
Primary Place of Performance: Florida International University
FL  US  33199-0001
Primary Place of Performance
Congressional District:
26
Unique Entity Identifier (UEI): Q3KCVK5S9CP1
Parent UEI: Q3KCVK5S9CP1
NSF Program(s): Robust Intelligence
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT

01002021DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9251, 7495, 1045
Program Element Code(s): 749500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Why do certain stories, but not others, resonate so powerfully with certain populations? Stories (a.k.a. narratives) are powerful: where rational argument fails, a single story can drive home a point, change a mind, and even change a life. What specific structures underlie the power of narrative, and what new artificial intelligence (AI) techniques are needed to learn these structures automatically so we can leverage them in applications? This project seeks to develop these new AI techniques to automatically uncover and confirm the fundamental structures underlying narrative, developing and testing with data drawn from the domains of education and culture. This work will be of broad relevance to developing more intelligent machines, understanding the mind and brain, and improving education. It will produce fundamental insights into a universal form of communication (narrative), providing a potentially transformative new set of tools to researcher and educators.

The project will develop new machine learning and natural language processing approaches to learning key aspects of narrative structure. The basic structure of a narrative involves the plot, a time-ordered sequence of important events, and the plot can be divided into three levels of structure: (1) plot pieces, (2) archetypal characters, and (3) narrative arcs. The PI and his students will first learn to extract these three types of narrative structure, the third of which (narrative arcs) is as-yet untried, using novel combinations of existing grammar learning approaches and Bayesian approaches, specifically the PI's Analogical Story Merging (ASM) algorithm, the Infinite Relational Model (IRM), and iterative learning. Second, the researchers will test hypotheses that reflect why specific stories are persuasive to specific cultures, and apply these insights to improving minority engagement in STEM and computing in middle-school classrooms in Miami Dade County Public Schools. Third, the researchers will seek to uncover systematic regularities in professional education cases (such as business cases, or medical case reports) that will lead to the ability to make computational predictions as to which cases should be most effective in the classroom.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Banisakher, Deya and Yarlott, W. Victor and Aldawsari, Mohammed and Rishe, Naphtali and Finlayson, Mark "Improving the Identification of the Discourse Function of News Article Paragraphs" 1st Joint Workshop on Narrative Understanding, Storylines, and Events (NUSE 2020) , 2020 https://doi.org/10.18653/v1/2020.nuse-1.3 Citation Details
Jahan, Labiba and Chauhan, Geeticka and Finlayson, Mark A "A New Approach to Animacy Detection" Proceedings of the 27th International Conference on Computational Linguistics , 2018 Citation Details
Aldawsari, Mohammed and Asgari, Ehsaneddin and Finlayson, Mark A. "Story Fragment Stitching: The Case of the Story of Moses" 1st Workshop on Artificial Intelligence for Narratives (AI4N 2020) , 2021 https://doi.org/ Citation Details
Akul Singh, Jared Hummer "pyTLEX: A Python Library for TimeLine EXtraction" , 2024 Citation Details
Aldawsari, Mohammed and Finlayson, Mark A "Detecting Subevents using Discourse and Narrative Features" Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019 10.18653/v1/P19-1471 Citation Details
Ocal, Mustafa and Singh, Akul and Hummer, Jared and Radas, Antonela and Finlayson, Mark "jTLEX: a Java Library for TimeLine EXtraction" , 2023 https://doi.org/10.18653/v1/2023.eacl-demo.4 Citation Details
Jahan, Labiba and Yarlott, W. Victor and Rahul, Mittal and Finlayson, Mark A. "Confirming the Generalizability of a Chain-Based Animacy Detector" 1st Workshop on Artificial Intelligence for Narratives (AI4N 2020) , 2021 https://doi.org/ Citation Details
Jahan, Labiba and Mittal, Rahul and Yarlott, W. Victor and Finlayson, Mark "A Straightforward Approach to Narratologically Grounded Character Identification" 28th International Conference on Computational Linguistics (COLING 2020) , 2020 https://doi.org/10.18653/v1/2020.coling-main.536 Citation Details
Jahan, Labiba and Mittal, Rahul and Finlayson, Mark "Inducing Stereotypical Character Roles from Plot Structure" Proceedings of the 25th Conference on Empirical Methods in Natural Language Process (EMNLP 2021) , 2021 https://doi.org/10.18653/v1/2021.emnlp-main.39 Citation Details
Aldawsari, Mohammed and Perez, Adrian and Banisakher, Deya and Finlayson, Mark "Distinguishing Between Foreground and Background Events in News" 28th International Conference on Computational Linguistics (COLING 2020) , 2020 https://doi.org/10.18653/v1/2020.coling-main.453 Citation Details
Jahan, Labiba and Finlayson, Mark "Character Identification Refined: A Proposal" Proceedings of the First Workshop on Narrative Understanding , 2019 10.18653/v1/W19-2402 Citation Details
(Showing: 1 - 10 of 11)

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