Computational Sensory Analysis of Creative Language

Sensory information in language enables us to share perceptual experiences and create a common understanding of the world around us. Especially in the creative language, which reveals itself in many forms, such as figurative language, persuasive or effective language, sensory factors impose a leveraging effect into semantic meaning by its expressive power. Although in the last decade, the studies focusing on the perceptual aspects of language have been thriving, automatic creative language analysis still suffers from the lack of perceptual grounding with its characteristics of vivid, non-literal and complex semantics. In this thesis, we propose the exploitation of the association between human senses and words as an external device to improve the computational linguistic models focusing on creative language. First, we present that sensory information reserved in the word meaning is obtainable by a distributional strategy over language. Second, we show that properly encoded sensory cues can enhance the automatic identification of figurative language. Finally, we argue that the exploitation of sensory information residing in linguistic modality in combination with the information coming from the perceptual modalities reinforces the computational assessment of multimodal creativity. We present a large scale sensory lexicon generation approach followed by its utilization in two main computational creativity experiments to confirm our arguments: 1) phrase-level and word-level metaphor identification in existing metaphor corpora; 2) creativity appreciation assessment in multimodal advertising prints incorporating the linguistic and visual modalities. The findings of the experiments show that sensory information is an invaluable indication of the creative aspect of the language and makes a significant contribution to the state of the art creative language analysis systems.