IC Colloquium : Computational Modelling of Metaphor
Event details
| Date | 10.02.2014 |
| Hour | 16:15 › 17:30 |
| Location | |
| Category | Conferences - Seminars |
Par : Ekaterina Shutova, UC Berkeley
IC Faculty candidate
Abstract
Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor plays a fundamental structural role in our cognition, helping us organise and project knowledge. For example, when we say “a well-oiled political machine”, we view the concept of political system in terms of a mechanism and transfer inferences from the domain of mechanisms onto our reasoning about political processes. Highly frequent in text, metaphorical language represents a significant challenge for natural language processing (NLP) systems; and large-scale, robust and accurate metaphor processing tools are needed to improve the overall quality of semantic interpretation in today's language technology.
In this talk I will introduce statistical models of metaphor and discuss how statistical techniques (in particular semi-supervised and unsupervised learning) can be applied to identify patterns of the use of metaphor in linguistic data and to generalize its higher-level mechanisms from text. I will then show what such systems can tell us about cross-cultural differences and opinion differences, as well as discussing how they can be extended and applied to a range of text processing problems.
Biography
Ekaterina Shutova is a Postdoctoral Research Fellow at the International Computer Science Institute (ICSI) and the Institute for Cognitive and Brain Sciences (ICBS) at the University of California, Berkeley, USA. Her research is in the area of Natural Language Processing with a specific focus on metaphor and human creativity, and its computational and cognitive modelling. She is currently leading the new Metaphor Extraction research team at ICSI, the goal of which is to create robust and accurate tools that identify metaphorical expressions in unrestricted text using machine learning and statistical techniques.
Previously, she was a Research Associate at DTAL and the Computer Laboratory, University of Cambridge, UK, where she worked on issues in computational lexical semantics. Ekaterina received her PhD in Computer Science from the University of Cambridge in 2011 and her doctoral dissertation concerned computational modelling of figurative language. She is a recipient of the Google Anita Borg Scholarship and a UK Government Overseas Research Students Award. She also serves as an Associate Editor of Cognitive Processing and is co-chairing a series of ACL workshops on Metaphor in NLP.
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IC Faculty candidate
Abstract
Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor plays a fundamental structural role in our cognition, helping us organise and project knowledge. For example, when we say “a well-oiled political machine”, we view the concept of political system in terms of a mechanism and transfer inferences from the domain of mechanisms onto our reasoning about political processes. Highly frequent in text, metaphorical language represents a significant challenge for natural language processing (NLP) systems; and large-scale, robust and accurate metaphor processing tools are needed to improve the overall quality of semantic interpretation in today's language technology.
In this talk I will introduce statistical models of metaphor and discuss how statistical techniques (in particular semi-supervised and unsupervised learning) can be applied to identify patterns of the use of metaphor in linguistic data and to generalize its higher-level mechanisms from text. I will then show what such systems can tell us about cross-cultural differences and opinion differences, as well as discussing how they can be extended and applied to a range of text processing problems.
Biography
Ekaterina Shutova is a Postdoctoral Research Fellow at the International Computer Science Institute (ICSI) and the Institute for Cognitive and Brain Sciences (ICBS) at the University of California, Berkeley, USA. Her research is in the area of Natural Language Processing with a specific focus on metaphor and human creativity, and its computational and cognitive modelling. She is currently leading the new Metaphor Extraction research team at ICSI, the goal of which is to create robust and accurate tools that identify metaphorical expressions in unrestricted text using machine learning and statistical techniques.
Previously, she was a Research Associate at DTAL and the Computer Laboratory, University of Cambridge, UK, where she worked on issues in computational lexical semantics. Ekaterina received her PhD in Computer Science from the University of Cambridge in 2011 and her doctoral dissertation concerned computational modelling of figurative language. She is a recipient of the Google Anita Borg Scholarship and a UK Government Overseas Research Students Award. She also serves as an Associate Editor of Cognitive Processing and is co-chairing a series of ACL workshops on Metaphor in NLP.
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Practical information
- Informed public
- Free
- This event is internal
Contact
- Tania Epars