Automatic Metaphor Recognition Based on Semantic Relation Patterns

Focusing on Chinese subject-predicate constructions, this paper analyzes the limitations of Selectional-Preference based metaphor recognition and proposes a new metaphor recognition model which is based on Semantic Relation Patterns. The model constructs Semantic Relation Pattern by integrating six types of semantic relations between a subject head and other subject heads in a subject-predicate cluster which share the same predicate head, and then employs a SVM classifier for metaphor recognition. Experiments show that the model outperforms the Selectional-Preference based metaphor recognition model to a great extent, achieving an F-1 of 89% in metaphor recognition, about 37% higher than Selectional-Preference based model. Analysis shows that the model is able to account for lexicalized metaphors, truth-condition literality and other types of literality and metaphor failed in Selectional-Preference based models. More importantly, the model can be generalized to unknown predicate heads. Theoretically, the semantic-relation-pattern model can also be applied in all endocentric constructions such as verb-objects and adjective-nouns.