Photo courtesy of eye2eye

Congratulations to Eric Baumer on successfully defending his Ph.D. thesis “Computational Metaphor Identification to Foster Critical Thinking and Creativity”

Committee:
Bill Tomlinson (Chair)
Lindsey Richland
Paul Dourish

The world’s longest abstract (verging on becoming a “concrete”)

Metaphor, the partial framing of a target concept in terms of a source
concept, permeates human thought and action. Metaphors often manifest
themselves as linguistic patterns in which language associated with a
source concept is used to describe a target concept. Any given metaphor
highlights some aspects of a concept while simultaneously downplaying
others. Novel metaphors can provide creative reframings of familiar
concepts by highlighting those aspects hidden by more common metaphors.
However, due to their ubiquity, conceptual metaphors can be difficult to
examine critically, if we can even notice them in the first place.

To address such difficulties, this dissertation develops computational
metaphor identification (CMI), which identifies potential conceptual
metaphors in written text. CMI maps selectional preferences of
relatively frequent nouns in a source corpus to those in a target
corpus. Such mappings indicate potential metaphors from concepts in the
source corpus to those in the target. CMI can be used to draw attention
to potential conceptual metaphors that might otherwise go unnoticed,
making those metaphors available for critical and creative examination.
For example, what might a given metaphor highlight, what might it hide,
and what alternative metaphors might frame the situation differently? In
this way, CMI is designed not as a type of computational reasoning, but
as a means of facilitating human reasoning.

To evaluate its capacity to foster critical thinking and creativity,
computational metaphor identification was incorporated into an
educational module about cell biology, which was used to perform an
experimental study in a 7th grade classroom. Students’ answers to
written questions about the cell were analyzed using CMI, and potential
metaphors were presented back to students. The results demonstrate that
the use of CMI effectively fostered both critical thinking about
metaphors and creative generation of alternative metaphors. These
results also speak to the varying roles of surface and structural
similarity in metaphorical reasoning, as well as the relationship
between noticing similarities and noticing differences when thinking
about metaphors. This evaluation not only demonstrates CMI’s usefulness
in educational contexts, but it also carries broader implications for
exploring the relationship between computation and human thought.

Congrats Dr. Eric!!