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While many different aspects of human experiences have been studied by theNLP community, none has captured its full richness. We propose a new task tocapture this richness based on an unlikely setting: movie characters. We soughtto capture theme-level similarities between movie characters that werecommunity-curated into 20,000 themes. By introducing a two-step approach thatbalances performance and efficiency, we managed to achieve 9-27\% improvementover recent paragraph-embedding based methods. Finally, we demonstrate how thethematic information learnt from movie characters can potentially be used tounderstand themes in the experience of people, as indicated on Reddit posts.

Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences