A Bayesian Mixed Effects Model of Literary Character

Bamman, David; Underwood, Ted; Smith, Noah A.

doi:10.3115/v1/p14-1035

Cited by 132 publications

(164 citation statements)

References 14 publications

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“…Coreference Disambiguation If the mention is a pronoun, we attempt to disambiguate it to a specific character using the coreference labels provided by BookNLP (Bamman et al, 2014).…”

Section: Mention→speakermentioning

confidence: 99%

A Two-stage Sieve Approach for Quote Attribution

Muzny¹,

Fang²,

Chang³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3 1 . Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our twostage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune either for overall performance or for the high precision appropriate for many use cases. Our system achieves an average F-score of 87.5% across three novels, outperforming previous systems, and can be tuned for precision of 90.4% at a recall of 65.1%.

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“…Coreference Disambiguation If the mention is a pronoun, we attempt to disambiguate it to a specific character using the coreference labels provided by BookNLP (Bamman et al, 2014).…”

Section: Mention→speakermentioning

confidence: 99%

A Two-stage Sieve Approach for Quote Attribution

Muzny¹,

Fang²,

Chang³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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“…3 https://www.gutenberg.org/ proposed which, given raw text, extract prototypical event structure (McIntyre and Lapata, 2010;Chambers and Jurafsky, 2009), prototypical characters (Bamman et al, 2013(Bamman et al, , 2014Elsner, 2012) and their social networks (Elson et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels

Frermann¹,

Szarvas²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation. Various models have addressed this task, but their evaluation has remained largely intrinsic and qualitative. Here, we propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations in an extrinsic fashion, assessing their ability to produce locally coherent groupings of novels (micro-clusters) in model space. We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations, and show that they i) yield better microclusters than less structured representations; and ii) are interpretable, and thus useful for further literary analysis or labelling of the emerging micro-clusters.

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“…The assignment of a character’s actions, received actions and attributes are used to describe the latent persona of a character. In Bamman, Underwood, and Smith (2014b), narratives extracted from a large collection of novels are used for the modeling of characters. In Skowron et al (2016) action movies character types are analyzed and classified using an integrative approach that complements linguistic analysis with interactive and communication characteristics.…”

Section: Related Workmentioning

confidence: 99%

Strategic Talk in Film

Payr

Skowron

Dobrosovestnova

et al. 2017

Cybernetics and Systems

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Conversational robots and agents are being designed for educational and/or persuasive tasks, e.g., health or fitness coaching. To pursue such tasks over a long time, they will need a complex model of the strategic goal, a variety of strategies to implement it in interaction, and the capability of strategic talk. Strategic talk is incipient ongoing conversation in which at least one participant has the objective of changing the other participant’s attitudes or goals. The paper is based on the observation that strategic talk can stretch over considerable periods of time and a number of conversational segments. Film dialogues are taken as a source to develop a model of the strategic talk of mentor characters. A corpus of film mentor utterances is annotated on the basis of the model, and the data are interpreted to arrive at insights into mentor behavior, especially into the realization and sequencing of strategies.

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A Bayesian Mixed Effects Model of Literary Character

Cited by 132 publications

References 14 publications

A Two-stage Sieve Approach for Quote Attribution

A Two-stage Sieve Approach for Quote Attribution

Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels

Strategic Talk in Film

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