David Bamman


2021

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Narrative Theory for Computational Narrative Understanding
Andrew Piper | Richard Jean So | David Bamman
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.

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Gender and Representation Bias in GPT-3 Generated Stories
Li Lucy | David Bamman
Proceedings of the Third Workshop on Narrative Understanding

Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.

2020

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Measuring Information Propagation in Literary Social Networks
Matthew Sims | David Bamman
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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. We describe a new pipeline for measuring information propagation in this domain and publish a new dataset for speaker attribution, enabling the evaluation of an important component of this pipeline on a wider range of literary texts than previously studied. Using this pipeline, we analyze the dynamics of information propagation in over 5,000 works of fiction, finding that information flows through characters that fill structural holes connecting different communities, and that characters who are women are depicted as filling this role much more frequently than characters who are men.

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Attending to Long-Distance Document Context for Sequence Labeling
Matthew Jörke | Jon Gillick | Matthew Sims | David Bamman
Findings of the Association for Computational Linguistics: EMNLP 2020

We present in this work a method for incorporating global context in long documents when making local decisions in sequence labeling problems like NER. Inspired by work in featurized log-linear models (Chieu and Ng, 2002; Sutton and McCallum, 2004), our model learns to attend to multiple mentions of the same word type in generating a representation for each token in context, extending that work to learning representations that can be incorporated into modern neural models. Attending to broader context at test time provides complementary information to pretraining (Gururangan et al., 2020), yields strong gains over equivalently parameterized models lacking such context, and performs best at recognizing entities with high TF-IDF scores (i.e., those that are important within a document).

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Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
David Bamman | Dirk Hovy | David Jurgens | Brendan O'Connor | Svitlana Volkova
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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An Annotated Dataset of Coreference in English Literature
David Bamman | Olivia Lewke | Anya Mansoor
Proceedings of the 12th Language Resources and Evaluation Conference

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 published between 1719 and 1922. This dataset differs from previous coreference corpora in containing documents whose average length (2,105.3 words) is four times longer than other benchmark datasets (463.7 for OntoNotes), and contains examples of difficult coreference problems common in literature. This dataset allows for an evaluation of cross-domain performance for the task of coreference resolution, and analysis into the characteristics of long-distance within-document coreference.

2019

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Literary Event Detection
Matthew Sims | Jong Ho Park | David Bamman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions—prestige and popularity—and demonstrate that there are statistically significant differences in the distribution of events for prestige.

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Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Svitlana Volkova | David Jurgens | Dirk Hovy | David Bamman | Oren Tsur
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

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Proceedings of the First Workshop on Narrative Understanding
David Bamman | Snigdha Chaturvedi | Elizabeth Clark | Madalina Fiterau | Mohit Iyyer
Proceedings of the First Workshop on Narrative Understanding

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An annotated dataset of literary entities
David Bamman | Sejal Popat | Sheng Shen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

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). These categories include non-named entities (such as “the boy”, “the kitchen”) and nested structure (such as [[the cook]’s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain literary data yields an improvement of over 20 absolute points in F-score (from 45.7 to 68.3), and mitigates a disparate impact in performance for male and female entities present in models trained on news data.

2018

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Telling Stories with Soundtracks: An Empirical Analysis of Music in Film
Jon Gillick | David Bamman
Proceedings of the First Workshop on Storytelling

Soundtracks play an important role in carrying the story of a film. In this work, we collect a corpus of movies and television shows matched with subtitles and soundtracks and analyze the relationship between story, song, and audience reception. We look at the content of a film through the lens of its latent topics and at the content of a song through descriptors of its musical attributes. In two experiments, we find first that individual topics are strongly associated with musical attributes, and second, that musical attributes of soundtracks are predictive of film ratings, even after controlling for topic and genre.

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Please Clap: Modeling Applause in Campaign Speeches
Jon Gillick | David Bamman
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause. In prediction experiments, we find that lexical features carry the most information, but that a variety of features are predictive, including prosody, long-term contextual dependencies, and theoretically motivated features designed to capture rhetorical techniques.

2017

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The Labeled Segmentation of Printed Books
Lara McConnaughey | Jennifer Dai | David Bamman
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.

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Adversarial Training for Relation Extraction
Yi Wu | David Bamman | Stuart Russell
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.

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Proceedings of the Second Workshop on NLP and Computational Social Science
Dirk Hovy | Svitlana Volkova | David Bamman | David Jurgens | Brendan O’Connor | Oren Tsur | A. Seza Doğruöz
Proceedings of the Second Workshop on NLP and Computational Social Science

2016

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Proceedings of the First Workshop on NLP and Computational Social Science
David Bamman | A. Seza Doğruöz | Jacob Eisenstein | Dirk Hovy | David Jurgens | Brendan O’Connor | Alice Oh | Oren Tsur | Svitlana Volkova
Proceedings of the First Workshop on NLP and Computational Social Science

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Beyond Canonical Texts: A Computational Analysis of Fanfiction
Smitha Milli | David Bamman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Open Extraction of Fine-Grained Political Statements
David Bamman | Noah A. Smith
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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CMU: Arc-Factored, Discriminative Semantic Dependency Parsing
Sam Thomson | Brendan O’Connor | Jeffrey Flanigan | David Bamman | Jesse Dodge | Swabha Swayamdipta | Nathan Schneider | Chris Dyer | Noah A. Smith
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Unsupervised Discovery of Biographical Structure from Text
David Bamman | Noah A. Smith
Transactions of the Association for Computational Linguistics, Volume 2

We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model. Taking as input timestamped text, we exploit latent correlations among events to learn a set of event classes (such as Born, Graduates High School, and Becomes Citizen), along with the typical times in a person’s life when those events occur. In a quantitative evaluation at the task of predicting a person’s age for a given event, we find that our generative model outperforms a strong linear regression baseline, along with simpler variants of the model that ablate some features. The abstract event classes that we learn allow us to perform a large-scale analysis of 242,970 Wikipedia biographies. Though it is known that women are greatly underrepresented on Wikipedia—not only as editors (Wikipedia, 2011) but also as subjects of articles (Reagle and Rhue, 2011)—we find that there is a bias in their characterization as well, with biographies of women containing significantly more emphasis on events of marriage and divorce than biographies of men.

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A Bayesian Mixed Effects Model of Literary Character
David Bamman | Ted Underwood | Noah A. Smith
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Distributed Representations of Geographically Situated Language
David Bamman | Chris Dyer | Noah A. Smith
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Learning Latent Personas of Film Characters
David Bamman | Brendan O’Connor | Noah A. Smith
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Framework for (Under)specifying Dependency Syntax without Overloading Annotators
Nathan Schneider | Brendan O’Connor | Naomi Saphra | David Bamman | Manaal Faruqui | Noah A. Smith | Chris Dyer | Jason Baldridge
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

2008

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The Annotation Guidelines of the Latin Dependency Treebank and Index Thomisticus Treebank: the Treatment of some specific Syntactic Constructions in Latin
David Bamman | Marco Passarotti | Roberto Busa | Gregory Crane
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The paper describes the treatment of some specific syntactic constructions in two treebanks of Latin according to a common set of annotation guidelines. Both projects work within the theoretical framework of Dependency Grammar, which has been demonstrated to be an especially appropriate framework for the representation of languages with a moderately free word order, where the linear order of constituents is broken up with elements of other constituents. The two projects are the first of their kind for Latin, so no prior established guidelines for syntactic annotation are available to rely on. The general model for the adopted style of representation is that used by the Prague Dependency Treebank, with departures arising from the Latin grammar of Pinkster, specifically in the traditional grammatical categories of the ablative absolute, the accusative + infinitive, and gerunds/gerundives. Sharing common annotation guidelines allows us to compare the datasets of the two treebanks for tasks such as mutually checking annotation consistency, diachronically studying specific syntactic constructions, and training statistical dependency parsers.

2007

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The Latin Dependency Treebank in a Cultural Heritage Digital Library
David Bamman | Gregory Crane
Proceedings of the Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2007).