Open Access
Understanding Genre in a Collection of a Million Volumes
Reads0
Chats0
About:
The article was published on 2014-01-01 and is currently open access. It has received 13 citations till now.read more
Citations
More filters
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
TL;DR: A simple but effective approach to WSD using a nearest neighbor classification on CWEs and it is shown that the pre-trained BERT model is able to place polysemic words into distinct 'sense' regions of the embedding space, while ELMo and Flair NLP do not seem to possess this ability.
Posted Content
Enriching BERT with Knowledge Graph Embeddings for Document Classification
TL;DR: Building upon BERT, a deep neural language model, it is demonstrated how to combine text representations with metadata and knowledge graph embeddings, which encode author information.
Journal ArticleDOI
The Life Cycles of Genres
TL;DR: The concept of genre is as old as literary theory itself, but centuries of debate haven’t produced much consensus on the topic.
Proceedings Article
Aida: Intelligent Image Analysis to Automatically Detect Poems in Digital Archives of Historic Newspapers.
TL;DR: An intelligent image analysis approach to automatically detect poems in digitally archived historic newspapers by integrating computer vision and machine learning to train an artificial neural network to determine whether an image has poetic text.
Poetry: Identification, Entity Recognition, and Retrieval
TL;DR: It is necessary to consider the role of language in the formation of identity and the role that language plays in the development of an individual's identity.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
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.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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.