2017
EACL
EACL 2017
A Data-Oriented Model of Literary Language
Abstract
AbstractWe consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 % of the variation in literary ratings.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Trend Setter
— Regression
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Keyword Pioneer
— literary analysis
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Regression
Natural Language Processing > Understanding > Syntax
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Core Methods > Feature Learning
Machine Learning > Learning Types > Regression