My work is dedicated to advancing our foundational understanding of human language. How do we understand what we hear and read? How are we able to convert thoughts into meaningful utterances that others understand? And how do we acquire the knowledge that makes all this possible. My research program sits at the intersection of artificial intelligence, psychology, and linguistics, and tackles these questions through theory, computationally implemented models of language, psychological experimentation, analysis of large linguistic datasets, and more.
My research focuses on linguistic meaning and its interactions with context in language understanding. Most of my work has focused on understanding how different types of linguistic context-dependence affect the way in which listeners exploit contextual information to efficiently approximate the speaker's meaning. To answer these questions, I combine insights from theoretical linguistics and cognitive science more broadly with experimental and computational methods.
My research combines Natural Language Processing (NLP), Computational Linguistics and Cognitive Science. I currently study what eye movements during reading can reveal about the linguistic knowledge and cognitive state of the reader, and how such signal can be used to improve NLP. Other related interests include multilingualism, linguistic typology, treebanking, and grounded language acquisition.
I am interested in how people use language to share their thoughts and feelings. I am particularly fascinated by the context-sensitivity of language understanding, issues of vagueness, and how people learn from linguistic messages. In my research, I use computational models and behavioral experiments and enjoy thinking up novel data analytic methods.
My research focuses on the nature, origins, and utility of abstract linguistic knowledge in children's early development, and how such knowledge grows to support adult language processing. In my research I use a combination of computational models from NLP, corpus studies, web-based experiments, and in-lab experiments. My postdoc is split with the Bergelson Lab at Duke University, where I conduct behavioral experiments with young children (12 - 36 months).
I'm interested in how children learn the meanings and structures of their native language, especially from the perspective of examining language in its social and cultural context. My approach focuses on Bayesian computational models and analysis of corpora and other large-scale datasets. I also work on tools to help research in the field be more scalable, reproducible, and open.
I'm interested in linguistic meaning: how it is acquired by the child, how it is structured in the mind of the speaker, and how it is worked out in the mind of the listener. I study these questions through different computational case studies, combining data and methods from linguistics, psychology, artificial intelligence, and neuroscience. You can find much more about my work on my website, where I also blog about language, cognitive science, and philosophy, among other things.
My research focuses on cognitive models of how language is perceived and acquired, with the goal of connecting these model to social and cultural processes to explain language structure. In particular, I am interested in how properties such as discreteness and compositionality arise in grounded communication systems that evolve over time. I pursue these questions by conducting behavioral experiments that mimic cultural evolutionary processes, and by building probabilistic models of the observed linguistic behavior.
My research develops computational models of how humans resolve ambiguity in language understanding, with the goal of building better systems of artificial intelligence. I am also interested in how brains and machines represent linguistic meaning and structure. I am supported by an NSF Graduate Research Fellowship and the NIH Program in Computationally-Enabled Integrative Neuroscience.
I’m interested in understanding how people process language, focusing in particular on the emergence of meaning from interaction. What speakers mean is often underspecified in what they actually say, and I want to understand how listeners infer the missing pieces of the puzzle. Recently, my main focus in addressing this rather broad question has been on ellipsis, in particular Verb Phrase Ellipsis. In some sense, elliptical utterances represent an extreme form of underspecification, but how the missing information is inferred remains highly controversial. I also work on the topic of inferential language comprehension from two other angles: the rational resolution of multiple implicature-driving forces; and a noisy-channel approach to non-literal interpretation.
I’m interested in the cognitive basis of human language. My current work combines behavioral experiments and computational models to investigate the relevance of linguistic knowledge in learning, reasoning, and judgment.
My research seeks to understand the cognitive underpinning of the production and comprehension of natural language. Speakers often face choices as to how to structure their intended message into an utterance. When multiple options are available, what general principles govern speaker choice? What inferences do comprehenders make about why something was said in a particular way? To answer these questions, I combine analysis of naturalistic language datasets, psycholinguistic experiments, and computational modeling.
In order to understand human intelligence, we need to understand how we can learn a mapping from language to meaning. Particularly, how do we come to associate language descriptions with relations and objects in a grounded environment, such as the real world? How can we use existing knowledge to infer the meanings of descriptions that are not easily exemplified? My approach is to construct computational models that learn this mapping as humans do. You can look at some of my work on my website.
I am a second year PhD student in the department of Linguistics at Harvard University, and a current visiting student at the Computational Psycholinguistics lab. My primary interest is uncovering what neural network language models learn about syntactic structures, and thinking more broadly about how hierarchical structure may be instantiated in distributed systems. I also do work on recursive models of pragmatics, investigating where they succeed and where they break down. Before Grad School I worked as a computational linguist and translator in New York. I did my undergraduate work at Stanford University, in the Symbolic Systems program and the Slavic Literature department.
I'm interested in how language use shapes human interaction and influences our thoughts and beliefs. One of my research projects looks at how the gender information conveyed by pronouns seems to introduce biases between production and comprehension.
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