Search Results for author:
Found 21 papers, 12 papers with code
Stylometric Literariness Classification: the Case of Stephen King
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.
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch Literature
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.
A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
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.
Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments
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.
Embarrassingly Simple Unsupervised Aspect Extraction
We present a simple but effective method for aspect identification in sentiment analysis.
Ranked #1 on
Aspect Category Detection
on Citysearch
What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models
Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers.
BERTje: A Dutch BERT Model
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks.
Ranked #3 on
Sentiment Analysis
on DBRD
Vector space explorations of literary language
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.
Cliche Expressions in Literary and Genre Novels
Should writers {``}avoid clich{\'e}s like the plague{''}?
Active DOP: A constituency treebank annotation tool with online learning
We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags.
German and French Neural Supertagging Experiments for LTAG Parsing
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars.
These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution
Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy.
A Data-Oriented Model of Literary Language
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
The Linguistic Annotation Framework (LAF) provides a general, extensible stand-off markup system for corpora.
Building a Corpus of Indefinite Uses Annotated with Fine-grained Semantic Functions
Natural languages possess a wealth of indefinite forms that typically differ in distribution and interpretation.
