Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Characterizing relationships beten people is fundamental for the understanding of narratives. In this work,  address the problem of inferring the polarity of relationships beten people in narrative summaries.  formulate the problem as a joint structured prediction for each narrative, and present a general model that combines evidence from linguistic and semantic features, as ll as features based on the structure of the social community in the text.  additionally provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation.
['Shashank Srivastava', 'Snigdha Chaturvedi', 'Tom M. Mitchell']
I am writing a research paper on a idea Inferring interpersonal relations in narrative summaries
[{"from": "human", "value": "I am writing a research paper on a idea Inferring interpersonal relations in narrative summaries"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Characterizing relationships beten people is fundamental for the understanding of narratives. In this work,  address the problem of inferring the polarity of relationships beten people in narrative summaries.  formulate the problem as a joint structured prediction for each narrative, and present a general model that combines evidence from linguistic and semantic features, as ll as features based on the structure of the social community in the text.  additionally provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation."}]