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Understanding Genre in a Collection of a Million Volumes

Ted Underwood
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The article was published on 2014-01-01 and is currently open access. It has received 13 citations till now.

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Proceedings Article

Smith at TREC2019: Learning to Rank Background Articles with Poetry Categories and Keyphrase Extraction.

TL;DR: Smith College participated in the TREC News Background Linking task in 2019 and constructed a linear learning to rank model trained on the 2018 data and submitted runs that included features derived from ongoing research into automatic poetry understanding.

Neutralising the Authorial Signal in Delta by Penalization: Stylometric Clustering of Genre in Spanish Novels.

TL;DR: John Burrows closes his paper with an unanswered question about why Delta works so well, and Jannidis and Lauer and Hoover show how Delta can be used to distinguish genre and periods within the works of a single author.
References
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Journal Article

Scikit-learn: Machine Learning in Python

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Book

ggplot2: Elegant Graphics for Data Analysis

TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Journal ArticleDOI

The WEKA data mining software: an update

TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.