Campus Event Calendar
Event Entry
What and Who
Computational Modelling of Human Language Comprehension
Vera Demberg
Cluster of Excellence - Multimodal Computing and Interaction - MMCI
Joint Lecture Series
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Wednesday, 3 February 2016
12:15
60 Minutes
E1 5
002
Saarbrücken
Abstract
Predicting the proto-typical "who", "what" and "where" of eating
Being able to learn information about proto-typical event representations, i.e., learning probability distributions over the typical "who"/"whom"/"how"/"where" for specific verbs has many applications in natural language understanding (e.g. inferring a location even if it's not mentioned) as well as cognitive modelling.
Consider for example the verb "to serve": a model also needs to encode which event participants typically occur together: waiters serve food in a restaurant, mum serves food at home, priests serve God in a church, and prisoners serve sentences in jail.
In this talk, I will give an overview of my group's current research in psycholinguistics, cognitive modelling and NLP and will then delve into more depth on recent models we have developed to address the above question. Specifically, I will discuss the use of large vector space models compared to neural networks on the task of predicting typical thematic role fillers based on large amounts of text.
Being able to learn information about proto-typical event representations, i.e., learning probability distributions over the typical "who"/"whom"/"how"/"where" for specific verbs has many applications in natural language understanding (e.g. inferring a location even if it's not mentioned) as well as cognitive modelling.
Consider for example the verb "to serve": a model also needs to encode which event participants typically occur together: waiters serve food in a restaurant, mum serves food at home, priests serve God in a church, and prisoners serve sentences in jail.
In this talk, I will give an overview of my group's current research in psycholinguistics, cognitive modelling and NLP and will then delve into more depth on recent models we have developed to address the above question. Specifically, I will discuss the use of large vector space models compared to neural networks on the task of predicting typical thematic role fillers based on large amounts of text.
Contact
Jennifer Müller
2900
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Jennifer Müller, 01/27/2016 08:24
Jennifer Müller, 09/01/2015 09:51 -- Created document.
Jennifer Müller, 09/01/2015 09:51 -- Created document.