In recent years, the task of generating realistic short and long texts have made tremendous
advancements. In particular, several recently proposed neural network-based language …

Statistical parsers are e ective but are typically limited to producing projective dependencies
or constituents. On the other hand, linguisti-cally rich parsers recognize non-local relations …

We 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 …

Abstract As Artificial Intelligent (AI) technologies become ubiquitous, humans will have to
contend with many benefits and disadvantages of these advancements. Particularly, in …

Abstract We describe Authorship Attribution of Bengali literary text. Our contributions include
a new corpus of 3,000 passages written by three Bengali authors, an end-to-end system for …

We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with
machine learning models show that it is possible to automatically distinguish novels that are …

This paper is a case-driven contribution to the discussion on the method-theory relationship
in practices within the field of Computational Literary Studies (CLS). Progress in this field …

The paper investigates impact and role of different feature types for the specific task of
Automatic Genre Classification with the final aim of identifying the most predictive ones. The …

Authorship Attribution is a long-standing problem in Natural Language Processing. Several
statistical and computational methods have been used to find a solution to this problem. In …

We present a discontinuous variant of treesubstitution grammar (tsg) based on Linear
Context-Free Rewriting Systems. We use this formalism to instantiate a Data-Oriented …