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Computational Linguistics


Title: Learning Language Through Interaction

Title and Abstract: TBA 

Bio: Bert Huang is an assistant professor in the Virginia Tech Department of Computer Science. He investigates machine learning with a special focus on models and data with structure stemming from natural networks. Within this focus, his work addresses open questions on theory, algorithms, and applications.

Title- Interpretable Machine Learning: What it means, How we're getting there


Note new time--12:30pm!

Defining and Parsing Universal Dependencies 

resMBS: Information Extraction, Topic Modeling and Tensor Factors for Financial Contracts

Abstract:

The Ins and Outs of Preposition Semantics: Challenges of Coverage and Cross-linguistic Adequacy


Title: Your ears deceive you: How categories (mis)shape perception, impede L2 learning, and what we can do about it

A question-answering system designed by researchers from UMD and the University of Colorado holds its own against a top human team.

Probabilistic models of human cognition have been widely successful at capturing the ways that people represent and reason with uncertain knowledge. In this talk I will explore the ways that this probabilistic approach can be applied to systematic and productive reasoning – in particular, natural language pragmatics and semantics. I will first describe how probabilistic programming languages provide a formal tool encompassing probabilistic uncertainty and compositional structure. I'll illustrate with a examples from inductive reasoning and social cognition.

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