Year: 2009
Type: conference-paper
Abstract: Psycholinguistic studies of metaphor processing must control their stimuli not just for word frequency but also for the frequency with which a term is used metaphorically. Thus, we consider the task of metaphor frequency estimation, which predicts how often target words will be used metaphorically. We develop metaphor classifiers which represent metaphorical domains through Latent Dirichlet Allocation, and apply these classifiers to the target words, aggregating their decisions to estimate the metaphorical frequencies. Training on only 400 sentences, our models are able to achieve 61.3% accuracy on metaphor classification and 77.8% accuracy on High vs. Low metaphorical frequency estimation.
Institutions: Stanford University, University of Colorado Boulder
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W2073700090