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.
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.
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.
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.
We present a simple but effective method for aspect identification in sentiment analysis.
Ranked #1 on Aspect Category Detection on Citysearch
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.
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
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.
We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags.
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars.
Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy.
The Linguistic Annotation Framework (LAF) provides a general, extensible stand-off markup system for corpora.
Natural languages possess a wealth of indefinite forms that typically differ in distribution and interpretation.