TY - JOUR
AU1 - Shutova, Ekaterina
AU2 - Teufel, Simone
AU3 - Korhonen, Anna
AB - Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modeling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, relies on the state-of-the-art parsing and lexical acquisition technologies (distributional clustering and selectional preference induction), and operates with a high accuracy.
TI - Statistical Metaphor Processing
JF - Computational Linguistics
DO - 10.1162/COLI_a_00124
DA - 2013-06-01
UR - https://www.deepdyve.com/lp/mit-press/statistical-metaphor-processing-mwUfY9IN5O
SP - 301
EP - 353
VL - 39
IS - 2
DP - DeepDyve
ER -