An Introduction to Variational Methods for Graphical Models
Machine Learning
The Journal of Machine Learning Research
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Thread detection in dynamic text message streams
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning of narrative schemas and their participants
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Natural Language Processing with Python
Natural Language Processing with Python
Extracting social networks from literary fiction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Computational Linguistics
Disentangling chat with local coherence models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Many works (of both fiction and non-fiction) span multiple, intersecting narratives, each of which constitutes a story in its own right. In this work I introduce the task of multiple narrative disentanglement (MND), in which the aim is to tease these narratives apart by assigning passages from a text to the sub-narratives to which they belong. The motivating example I use is David Foster Wallace's fictional text Infinite Jest. I selected this book because it contains multiple, interweaving narratives within its sprawling 1,000-plus pages. I propose and evaluate a novel unsupervised approach to MND that is motivated by the theory of narratology. This method achieves strong empirical results, successfully disentangling the threads in Infinite Jest and significantly outperforming baseline strategies in doing so.