Speech & Natural Language Processing Group

"Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running
text as either being used metaphorically or not. We start by examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline."

We present a supervised machine learning system
for word-level classification of all content
words in a running text as being metaphorical
or non-metaphorical. The system provides
a substantial improvement upon a previously
published baseline, using re-weighting of the
training examples and using features derived
from a concreteness database. We observe that
while the first manipulation was very effective,
the second was only slightly so. Possible
reasons for these observations are discussed.