Modeling covert event retrieval in logical metonymy: probabilistic and distributional accounts


Logical metonymies (The student finished the beer) represent a challenge to compositionality since they involve semantic content not overtly realized in the sentence (covert events → drinking the beer). We present a contrastive study of two classes of computational models for logical metonymy in German, namely a probabilistic and a distributional, similarity-based model. These are built using the SDEWAC corpus and evaluated against a dataset from a self-paced reading and a probe recognition study for their sensitivity to thematic fit effects via their accuracy in predicting the correct covert event in a metonymical context. The similarity-based models allow for better coverage while maintaining the accuracy of the probabilistic models.

Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)
Alessandra Zarcone
Alessandra Zarcone
Professor of Language Technologies and Cognitive Assistants

Computational linguist with a background in NLP and in psycholinguistics, working on AI, NLP and human-machine interaction.