Unsupervised Learning of Evolving Relationships Between Literary Characters

By Snigdha Chaturvedi, Mohit Iyyer, Hal Daumé Iii. 2017

60 citations recorded by the source corpus.

Understanding inter-character relationships is fundamental for understanding character intentions and goals in a narrative. This paper addresses unsupervised modeling of relationships between characters. We model relationships as dynamic phenomenon, represented as evolving sequences of latent states empirically learned from data. Unlike most previous work our approach is completely unsupervised. This enables data-driven inference of inter-character relationship types beyond simple sentiment polarities, by incorporating lexical and semantic representations, and leveraging large quantities of raw text. We present three models based on rich sets of linguistic features that capture various cues about relationships. We compare these models with existing techniques and also demonstrate that relationship categories learned by our model are semantically coherent.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

The NarrativeQA Reading Comprehension Challenge (2017, background): “…by inducing character types (Bamman et al., 2014b; Bamman et al., 2014a), understanding relationships between characters (Iyyer et al., 2016; Chaturvedi et al., 2017), or understanding plans, goals, and narrative structure in terms of abstract narratives (Schank and Abelson, 1977; Wilensky,…”
Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels (2017, methodology): “…al., 2013), prototypical plot templates (tropes, e.g., ‘corrupt corporate executive’) or manually created gold-standards of character types (Vala et al., 2016) or their relations (Massey et al., 2015; Chaturvedi et al., 2017) to automatically measure the intrinsic value of learnt representations.”
An annotated dataset of literary entities (2019, background): “Much of this work relies on the ability to extract entities accurately, including work focused on modeling (Bamman et al., 2014; Iyyer et al., 2016; Chaturvedi et al., 2017).”
Entity-Centric Contextual Affective Analysis (2019, background): “…power, agency, and sentiment framework, initial approaches to person-centric analyses used graphical models to identify personas in narratives (Bamman et al., 2013; Card et al., 2016; Iyyer et al., 2016; Chaturvedi et al., 2017), where personas are distributions over nouns, adjectives and verbs.”
Story Comprehension for Predicting What Happens Next (2017, unknown): “…(Bamman et al., 2013, 2014) or Prop-pian (Propp, 1968) roles (Valls-Vargas et al., 2014, 2015), inter-character relationships (Iyyer et al., 2016; Chaturvedi et al., 2016, 2017), and social networks of characters (Elson et al., 2010; Elson, 2012; Agarwal et al., 2013, 2014; Krishnan and…”