Project Grant 2517821
- This $240,470 Project Grant awarded by the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) aims to investigate evidential markers - the grammatical coding of information source and speaker's perspectives - across different languages. The project's goal is to create treebanks/universal dependencies corpora to develop an automated deep-learning natural language processing (NLP) model that can extract information from texts...
- This $449,275 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to advance the scientific understanding of human language processing and change. The project, led by the University of California, Davis (UC Davis), combines expertise from linguistics, psychology, and computational approaches to investigate how language users integrate general language constraints and verb-specific preferences...
- This National Science Foundation (NSF) Project Grant award under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $268,067 to The Research Foundation For The State University Of New York (RF SUNY) to advance linguistic research through the development of speech technologies. The key products and services delivered under this 2-year award, which runs from August 1, 2025 to July 31, 2027, include: Creation of benchmark datasets, comprehensive lexica, and advanced data...
- This National Science Foundation (NSF) award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $299,977 to Trustees of Indiana University to develop five new syntactically-annotated corpora of endangered languages. The project aims to build high-quality datasets and computational tools for analyzing the grammars of these languages, validating new techniques that can work with small datasets. The key products include: (1) five freely-available treebanks...
- This National Science Foundation (NSF) award under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $473,434 to Harrisburg University of Science and Technology to conduct research on cross-linguistic semantic priming effects in 45 languages and compare them to large language models. The project aims to build a large-scale dataset to understand how recognizing one word can improve recognition of a conceptually related word, known as semantic priming, and examine how...
- This $116,797 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to document, analyze, and revitalize a contact language that has undergone significant grammatical change. The project will record 40 hours of natural speech through sociolinguistic interviews and linguistic elicitation, transcribe and annotate the data, and develop an open-access digital corpus. Grounded in theories of...
- This Project Grant award from the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $104,114 to Haverford College to conduct research on the mechanisms underlying human language learning. The research aims to better understand how people construct mental representations of words and learn the morphological and phonological patterns that govern language. The project will develop interpretable computational models and algorithms...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant awarded to the University of Arizona, doing business as the Arizona Board of Regents, will develop five new Universal Dependencies (UD) corpora of endangered languages totaling at least 30,000 tokens each. The $139,306 award, spanning August 2023 to July 2026, will produce: (1) freely-available, fully-annotated treebanks for quantitative linguistic analyses and natural language...
- This Project Grant award of $249,993 from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) supports a research project at Georgetown University to examine language data and advance language infrastructure, knowledge, and theory. The project provides empirical data for developing and testing theories of language, which are important for understanding human cognition, and trains students in these methods. The research focuses on documenting and...
- This Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program examines the relationship between information structure and syntax across diverse languages. The $237,402 award to The Ohio State University will investigate how context, word order, and emphasis affect language processing and understanding in conversational settings. The research methodology utilizes cognitive data, including eye-tracking, to study how...
COLLABORATIVE RESEARCH: ADVANCING OPEN-ACCESS RESOURCES FOR LANGUAGES: INVESTIGATING EVIDENTIALITY AND FOCUS FEATURES -THIS PROJECT INVESTIGATES EVIDENTIAL MARKERS, THE GRAMMATICAL CODING OF INFORMATION SOURCE AND SPEAKER?S PERSPECTIVES. EVIDENTIAL MARKERS INDICATE WHETHER A STATEMENT IS BASED ON DIRECT OBSERVATION, INFERENCE, HEARSAY, OR OTHER TYPES OF EVIDENCE. EXAMINING HOW DIFFERENT LANGUAGES EXPRESS SOURCE OF INFORMATION CAN PROVIDE INSIGHTS INTO HOW SPEAKERS EVALUATE INFORMATION, REVEALING DEEPER COGNITIVE PROCESSES. THE PROJECT'S GOAL IS TO CREATE TREEBANKS/UNIVERSAL DEPENDENCIES CORPORA TO DEVELOP AN AUTOMATED DEEP-LEARNING NATURAL LANGUAGE PROCESSING (NLP) MODEL. CONSIDERING THE DISTRIBUTIONAL CHARACTERISTICS AROUND EVIDENTIAL MARKERS, THE MODEL WILL BE ABLE TO EXTRACT INFORMATION FROM TEXTS SEMI-AUTOMATICALLY. THE PROJECT EXPLORES A KEY LINGUISTIC QUESTION: WHY EVIDENTIAL MARKERS IN SOME LANGUAGES BEHAVE DIFFERENTLY FROM THOSE IN OTHER LANGUAGES, AS THEY SEEM TO HAVE MORE THAN ONE FUNCTION AND ALLOW MORE FLEXIBILITY IN HOW THEY APPEAR IN SENTENCES. THIS FLEXIBILITY MAY AFFECT HOW LANGUAGE USERS SHOW EVIDENCE OR EMPHASIS IN LONGER CONVERSATIONS. THE WORK WILL NOT ONLY EXPAND UNDERSTANDING OF HOW THESE LANGUAGES WORK BUT ALSO SUPPORT LANGUAGE TECHNOLOGY DEVELOPMENT. BECAUSE THE PROJECT WILL USE A DEEP LEARNING ARCHITECTURE TO DEVELOP THIS NLP MODEL, THERE ARE SEVERAL OTHER POTENTIAL APPLICATIONS BEYOND THE PROPOSED LINGUISTICS GOALS: THE DEVELOPMENT OF MACHINE TRANSLATION TOOLS; TEXT SUMMARIZATION; AND OTHER TYPES OF HUMAN-MACHINE INTERACTIONS (E.G. CHATBOTS) AND AI SYSTEMS. 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.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $57.8k | 8/21/25 |