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Cutting a Cake with a Hammer in Vector Space: Models of Thematic Fit
Asad Sayeed - University of the Saarland
Wednesday, April 27, 2016, 1:30-2:30 pm Calendar
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Abstract

Thematic fit is the extent to which a noun satisfies the selectional
preference  of a verb. Thematic fit can be measured via human judgement
studies by asking how strongly subjects agree that aparticular noun
fulfills a semantic role of a verb.  For example, subjects respond with
a high rating - that lunch is a thing that is eaten, and with a low
rating - that lunch could be doing the eating. There are many judgements,
however, that are much less clear-cut.

I have developed models of thematic fit that have high correlation with
human-collected judgements using, initially, the Distributional Memory
(DM) approach of Baroni and Lenci (2010).  This method involves creating
an order-3 tensor consisting of two vocabulary axes and one semantic
dependency-link axis. This mostly-unsupervised model can be used to
produce machine judgements that correlate well with human judgements
across multiple data sets.

I will also discuss corpus resources and tools as well as work in
transitioning to neural networks.  I will motivate this work in
the context of incremental semantic parsing and the representation of
event sequence knowledge (scripts). This work has the potential to
explore  the boundaries between "distributional" knowledge
and  "formal" knowledge in the semantic processing of language users.

Bio

Asad Sayeed graduated with a Ph. D. in Computer Science in 2011. He was a
member of the CLIP Lab from 2004 through 2011 and his advisor was Amy
Weinberg. He is a member of the Multimodal Computing and Interaction
Cluster of Excellence at Saarland University in Saarbrücken, Germany. His
research and teaching are at the intersection of NLP, psycholinguistics,
and formal linguistics, with a particular focus on the interface between
syntax, semantics, and the "rest-of-cognition" as it pertains to human
interaction.

This talk is organized by Naomi Feldman