Search Results for author: Andreas van Cranenburgh

Found 21 papers, 12 papers with code

Stylometric Literariness Classification: the Case of Stephen King

1 code implementation EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 Andreas van Cranenburgh, Erik Ketzan

This paper applies stylometry to quantify the literariness of 73 novels and novellas by American author Stephen King, chosen as an extraordinary case of a writer who has been dubbed both “high” and “low” in literariness in critical reception.

Classification

A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch Literature

1 code implementation CRAC (ACL) 2021 Andreas van Cranenburgh, Esther Ploeger, Frank van den Berg, Remi Thüss

We introduce a modular, hybrid coreference resolution system that extends a rule-based baseline with three neural classifiers for the subtasks mention detection, mention attributes (gender, animacy, number), and pronoun resolution.

Coreference Resolution Feature Engineering

A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

1 code implementation COLING (CRAC) 2020 Corbèn Poot, Andreas van Cranenburgh

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text.

Coreference Resolution

Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments

1 code implementation COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 Andreas van Cranenburgh, Corina Koolen

In an exploratory analysis, we compare the ratings to those from the large reader survey of the Riddle in which social factors were not excluded, and to machine learning predictions of those literary ratings.

BIG-bench Machine Learning

BERTje: A Dutch BERT Model

2 code implementations19 Dec 2019 Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, Tommaso Caselli, Gertjan van Noord, Malvina Nissim

The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks.

Language Modelling named-entity-recognition +4

Vector space explorations of literary language

1 code implementation Language Resources and Evaluation 2019 Andreas van Cranenburgh, Karina van Dalen-Oskam, Joris van Zundert

Previous research showed that ratings of literariness are predictable from texts to a substantial extent using machine learning, suggesting that it may be possible to explain the consensus among readers on which novels are literary as a consensus on the kind of writing style that characterizes literature.

Topic Models

Active DOP: A constituency treebank annotation tool with online learning

1 code implementation COLING 2018 Andreas van Cranenburgh

We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags.

Active Learning Feature Engineering +1

A Data-Oriented Model of Literary Language

1 code implementation EACL 2017 Andreas van Cranenburgh, Rens Bod

We consider the task of predicting how literary a text is, with a gold standard from human ratings.

LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible

1 code implementation1 Oct 2014 Dirk Roorda, Gino Kalkman, Martijn Naaijer, Andreas van Cranenburgh

The Linguistic Annotation Framework (LAF) provides a general, extensible stand-off markup system for corpora.

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