The Actor-Topic Model for Extracting Social Networks in Literary Narrative

We present a generative model for conversational dialogues, namely the actortopic model (ACTM), that extend the author-topic model (Rosen-Zvi, et.al, 2004) to identify actors of given conversation in literary narratives. Thus ACTM assigns each instance of quoted speech to an appropriate character. We model dialogues in a literary text, which take place between two or more actors conversing on different topics, as distributions over topics, which are also mixtures of the term distributions associated with multiple actors. This follows the linguistic intuition that rich contextual information can be useful in understanding dialogues, eventually effecting the social network construction. We propose ACTM to ideally lead our research on social network extraction in literary narratives. Our experiments on nineteenth century English novels indicate that exploiting content structure of dialogues can yield significant improvements over a baseline using language models which is based on local context in constructing social interactions.