Year: 2020
Type: conference-paper
Abstract: This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection.We participate in all four phases of competition with both datasets, i.e.Verbs and All-POS on the VUA and the TOFEL datasets.We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context.Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5% higher than the strong baselines.The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.
Institutions: Hong Kong Polytechnic University, Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Hong Kong Polytechnic University
OpenAlex ID: W3045508562