mining for meaning
Words have a particular meaning. While language users have no problems inferring those meanings, this is a difficult task for a computer system. I investigate whether and how a computer might be able to automatically extract meaning from large text collections.
An important part of the research focuses on dimensionality reduction, and its application to meaning extraction. The use of large text collections brings about a large number of contexts in which a word occurs. Using a mathematical dimensionality reduction, the abundance of individual contexts can be reduced to a limited number of significant dimensions, containing latent semantics.
Another part of the research focuses on the use of tensors for natural language processing. Language can often be described as multi-way co-occurrences, and tensors provide a natural framework to capture and analyze such multi-way co-occurrences.
My research has applications in various natural language processing tasks such as word sense induction and selectional preference acquisition.