NSF Org:	BCS Division of Behavioral and Cognitive Sciences
Recipient:	UNIVERSITY OF ROCHESTER
Initial Amendment Date:	August 8, 2022
Latest Amendment Date:	August 8, 2022
Award Number:	2214933
Award Instrument:	Standard Grant
Program Manager:	Wilson De Lima Silva widelima@nsf.gov  (703)292-7096 BCS  Division of Behavioral and Cognitive Sciences SBE  Directorate for Social, Behavioral and Economic Sciences
Start Date:	August 15, 2022
End Date:	July 31, 2026 (Estimated)
Total Intended Award Amount:	$439,222.00
Total Awarded Amount to Date:	$439,222.00
Funds Obligated to Date:	FY 2022 = $439,222.00
History of Investigator:	Joanna Pietraszko (Principal Investigator) joannapietraszko@gmail.com Aaron White (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Rochester 910 GENESEE ST ROCHESTER NY  US  14611-3847 (585)275-4031
Sponsor Congressional District:	25
Primary Place of Performance:	University of Rochester 518 HYLAN, RC BOX 270140 Rochester NY  US  14627-0140
Primary Place of Performance Congressional District:	25
Unique Entity Identifier (UEI):	F27KDXZMF9Y8
Parent UEI:
NSF Program(s):	DLI-Dyn Language Infrastructur, Linguistics
Primary Program Source:	01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	1311, 5928, 7719, 9251, SMET
Program Element Code(s):	122Y00, 131100
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.075

ABSTRACT

This project investigates inferences that can be drawn from sentences with subordinate clauses, such as 'I forgot to lock the door' or 'I forgot locking the door'. It has been observed that languages use specific verb forms to encode specific inferences. For instance, the form 'to lock' above implies that the door wasn?t locked, while using the form 'locking' implies the opposite. Such mappings between form and meaning/inference have been investigated in English as well as a small number of other closely related languages, and these investigations have led to interesting hypotheses about how human language connects form with meaning in general. The current picture is incomplete, however, since it is not clear whether these hypotheses cover languages outside the circumscribed collection on which they were developed. This project aims to address this gap by investigating the topic in an understudied and under-resourced language whose subordinate clauses differ in important ways from those found in well-documented languages.

The project has two main components: a large-scale data collection component and a computational modeling component. The data collection component uses a novel staged elicitation methodology that interleaves standard elicitation methodologies with both corpus-based methods and web-based experiments. The computational modeling component develops a computational model for finding underlying semantic classes of predicates across multiple unrelated languages. This research not only helps to reveal a fuller picture of how human language connects form with meaning in general; it also serves to develop resources crucial for developing applied technologies for natural language understanding.

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.

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