Computer Science Seminar: Janet Pierrehumbert (Oxford University)
Speaker: Janet Pierrehumbert (Oxford University)
Title: Bringing Time and Social Space into Natural Language Processing
The seminar will be available for in-person and Zoom participation. If you would like to receive the Zoom link, please register using the “Register” button above.
Human languages have extremely large vocabularies, and by assembling words into sequences, humans can express complex and novel ideas to each other. The likelihood of selecting any given word at any point in time varies greatly as a function of the context. Understanding and formalizing these contextual influences is essential for building robust and adaptable NLP systems. This talk will focus on two sources of variability: variation over time, and variation across speakers. I will first consider the how individual words behave as a function of who is speaking and what the topic of discussion is. I will explain how vector space representations of words (also known as word embeddings) make it possible to investigate the abstract concepts that underpin the use of groups of semantically related words. Finally, using social networks defined from social media posts, I will illustrate how graph neural networks can be used to investigate the structure and dynamics of opinions in the social space.
Professor Janet Pierrehumbert has an interdisciplinary background from Harvard and MIT in linguistics, mathematics, and electrical engineering and computer science. Her PhD dissertation developed a model of English intonation that was applied to generate pitch contours in synthetic speech. She began her career as a Member of Technical Staff at AT&T Bell Laboratories in Linguistics and Artificial Intelligence Research. From there, Pierrehumbert moved to Northwestern University, where she headed a research group that used experimental and computational methods to understand lexical systems in English and many other languages. Pierrehumbert joined the University of Oxford faculty in 2015 as Professor of Language Modelling in the Oxford e-Research Centre, Her current focusses on robust and interpretable natural language processing methods, in particular ones that can handling variation across different topics, topics, and social contexts. She has held visiting appointments at Stanford, the Royal Institute of Technology, the École Normale Superieure, and the University of Canterbury.
Pierrehumbert is a Member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, a Fellow of the Cognitive Science Society and a Fellow of the Linguistic Society of America. She won the Medal for Scientific Achievement of the International Speech Communication Association (ISCA) in 2020.