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Losing Shahrazad: A Distant Reading of 1001 Nights
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In: Senior Projects Spring 2018 (2018)
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Modeling Events and Affects in Social Media Stories
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In: Rahimtoroghi, Elahe. (2018). Modeling Events and Affects in Social Media Stories. UC Santa Cruz: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/6tk1758v (2018)
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Abstract:
Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank’s definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections.In our final contribution, we develop supervised methods for modeling the protagonist’s goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.
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Keyword:
Computational Linguistics; Computer science; Narrative; Natural Language Processing
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URL: http://www.escholarship.org/uc/item/6tk1758v
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Simplicity and informativeness in semantic category systems ...
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#Hashtags ... : A Look at the Evaluative Roles of Hashtags on Twitter ...
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Implicit And Explicit Aspect Extraction In Financial Microblogs ...
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Implicit And Explicit Aspect Extraction In Financial Microblogs ...
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11 |
Learning distributions as they come: Particle filter models for online distributional learning of phonetic categories ...
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A Markedly Different Approach ... : Investigating PIE Stops Using Modern Empirical Methods ...
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Innovative Implementation of a Web-Based Rating System for Individualizing Online English Speaking Instruction
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In: English Publications (2018)
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Parsing Natural Language Queries for Extracting Data from Large-Scale Geospatial Transportation Asset Repositories
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In: English Conference Papers, Posters and Proceedings (2018)
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OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification
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In: English Conference Papers, Posters and Proceedings (2018)
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16 |
Multiplex model of mental lexicon reveals explosive learning in humans ...
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The multiplex structure of the mental lexicon influences picture naming in people with aphasia ...
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Communicative Efficiency, Uniform Information Density, and the Rational Speech Act theory ...
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Can prediction-based distributional semantic models predict typicality? ...
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Cohort and rhyme priming emerge from the multiplex network structure of the mental lexicon ...
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