Topic model analysis of metaphor frequency for psycholinguistic stimuli

Bethard, Steven; Lai, Vicky T.; Martin, James

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Topic model analysis of metaphor frequency for psycholinguistic stimuli

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

Bethard_2009.pdf
(Publisher version), 157KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Bethard, S., Lai, V. T., & Martin, J. (2009). Topic model analysis of metaphor frequency for psycholinguistic stimuli. In Proceedings of the NAACL HLT Workshop on Computational Approaches to Linguistic Creativity, Boulder, Colorado, June 4, 2009 (pp. 9-16). Stroudsburg, PA: Association for Computational Linguistics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0012-CA4D-B

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.