LIN14: Computational Cognitive Models of Meaning
About
We study and develop cognitively plausible computational representations of linguistic meaning and algorithms to process those representations.
Active since: 2021
Closed Group of Collaborators
- University of Rochester
- Cornell University
Collaborative Goals
Our primary goal is to share information and develop research collaborations between research groups in central NY that study linguistic meaning using computational and experimental methods.
To this end, we organize an annual workshop, which enables all participants to discuss ongoing work in this area, provides a venue for students to network with their peers, and facilitates collaboration between students and faculty in the region.
Group Organizers
Group Members
- John R. Starr Cornell University Linguistics PhD Student
- Arsal Imtiaz University of Rochester Computer Science Masters Student
- Alex Martin University of Rochester Computer Science Undergraduate Student
- Will Gantt University of Rochester Computer Science PhD Student
- Lisa Zhao Cornell University Linguistics PhD Student
- Nianpo Su Cornell University Linguistics PhD Student
- Siwei Tang (Stephanie) University of Rochester Linguistics Masters Student
- Pavlo Kuchmiichuk University of Rochester Linguistics Masters Student
- Jin Dou University of Rochester Biomedical Engineering PhD Student
- Becca Sanaeikia University of Rochester Philosophy PhD Student
- Fangcong Yin Cornell University Information Science Undergraduate Student
- Zixu Wang University of Rochester Linguistics Masters Student
- Siddharth Vashishtha University of Rochester Computer Science PhD Student
- Helena Aparicio Cornell University Linguistics Pre-Tenure Faculty
- Kaelyn Lamp Cornell University Linguistics PhD Student
- Kihyo Park Cornell University Linguistics PhD Student
- Marten van Schijndel Cornell University Linguistics Pre-Tenure Faculty
- Ben Kane University of Rochester Computer Science PhD Student
- Congwen Zhang University of Rochester Linguistics Masters Student
- Hannah An University of Rochester Computer Science PhD Student
- Jack Valinsky University of Rochester Linguistics Masters Student
- Stephanie Richter University of Rochester Linguistics PhD Student
- Psyche He Cornell University Psychology PhD Student
- Jiayue Meng University of Rochester Data Science Undergraduate Student
- Scott Grimm University of Rochester Linguistics Tenured Faculty
- Jeff Runner University of Rochester Linguistics Tenured Faculty
- Julian Grove University of Rochester Linguistics Postdoctoral Researcher
- Aaron Steven White University of Rochester Linguistics Pre-Tenure Faculty
Non Corridor Members
- Debasmita Bhattacharya Columbia University Computer Science PhD Student
- Lilia Rissman University of Wisconsin Psychology Research Scientist
Activities
Processing and Evaluating Event Representations
March 31, 2023, 9 a.m.
Workshop on Computational Meaning Representations
April 1, 2022, 10 a.m.
Group Outcomes
The ability to bring in external speakers (Lilia Rissman) allowed us to expand our research network.
Second Workshop on Processing and Evaluating Event Representations (PEER2023)
The Second Workshop on Processing and Evaluating Event Representations (PEER2023) was held on March 31, 2023 at the University of Rochester. The workshop aimed to bring together researchers working on computational models of incremental language understanding with researchers working on event semantics from both a computational and experimental perspective. Presentations will focus on a variety of questions in this domain: (a) what symbolic and continuous representations of linguistic meanings are necessary for capturing different aspects of linguistic meaning?; (b) how do we determine the the psycholinguistic validity of such representations to better understand incremental processing in humans?; and (c) how might the psycholinguistically valid representations be deployed during incremental language processing? Special focus is placed on meaning representations that take the concept of an event as a core organizing principle.