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Found 32 papers, 18 papers with code
Narrative Theory for Computational Narrative Understanding
Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection.
Gender and Representation Bias in GPT-3 Generated Stories
Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes.
Grounding Characters and Places in Narrative Texts
Tracking characters and locations throughout a story can help improve the understanding of its plot structure.
Dramatic Conversation Disentanglement
We present a new dataset for studying conversation disentanglement in movies and TV series.
Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4
In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query.
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups.
Predicting Long-Term Citations from Short-Term Linguistic Influence
A standard measure of the influence of a research paper is the number of times it is cited.
Discovering Differences in the Representation of People using Contextualized Semantic Axes
A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances.
Characterizing English Variation across Social Media Communities with BERT
Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups.
Attending to Long-Distance Document Context for Sequence Labeling
We present in this work a method for incorporating global context in long documents when making local decisions in sequence labeling problems like NER.
Latin BERT: A Contextual Language Model for Classical Philology
We present Latin BERT, a contextual language model for the Latin language, trained on 642. 7 million words from a variety of sources spanning the Classical era to the 21st century.
Measuring Information Propagation in Literary Social Networks
We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text.
Breaking Speech Recognizers to Imagine Lyrics
We introduce a new method for generating text, and in particular song lyrics, based on the speech-like acoustic qualities of a given audio file.
An Annotated Dataset of Coreference in English Literature
We present in this work a new dataset of coreference annotations for works of literature in English, covering 29, 103 mentions in 210, 532 tokens from 100 works of fiction.
Literary Event Detection
In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel.
An annotated dataset of literary entities
We present a new dataset comprised of 210, 532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle).
Learning to Groove with Inverse Sequence Transformations
We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models.
Please Clap: Modeling Applause in Campaign Speeches
This work examines the rhetorical techniques that speakers employ during political campaigns.
Telling Stories with Soundtracks: An Empirical Analysis of Music in Film
Soundtracks play an important role in carrying the story of a film.
Adversarial Training for Relation Extraction
Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.
The Labeled Segmentation of Printed Books
We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books.
Annotating Character Relationships in Literary Texts
We present a dataset of manually annotated relationships between characters in literary texts, in order to support the training and evaluation of automatic methods for relation type prediction in this domain (Makazhanov et al., 2014; Kokkinakis, 2013) and the broader computational analysis of literary character (Elson et al., 2010; Bamman et al., 2014; Vala et al., 2015; Flekova and Gurevych, 2015).
Unsupervised Discovery of Biographical Structure from Text
We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model.
A framework for (under)specifying dependency syntax without overloading annotators
We introduce a framework for lightweight dependency syntax annotation.
New Alignment Methods for Discriminative Book Summarization
We consider the unsupervised alignment of the full text of a book with a human-written summary.
Gender identity and lexical variation in social media
Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers.
