Modeling Narrative Discourse

This thesis describes new approaches to the formal modeling of narrative discourse. Although narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coherence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such a measure of similarity to a more traditional one based on the semantic distances between story propositions

Extending and Evaluating a Platform for Story Understanding

We summarize recent developments in our platform for symbolically representing and reasoning over human narratives. The expressive range of the system is bolstered by the infusion of a large library of knowledge frames, including verbs, adjectives, nouns and adverbs, from external linguistic resources. Extensions to the model itself include alternate timelines (imagined states for goals, plans, beliefs and other modalities), hypotheticals, modifiers and connections between instantiated frames such as causality. We describe a corpus collection experiment that evaluates the usability of the graphical encoding interface, and measure the inter-annotator agreement yielded by our novel representation and tool

A Lightweight Intelligent Virtual Cinematography System for Machinima Production

Machinima is a low-cost alternative to full production filmmaking. However, creating quality cinematic visualizations with existing machinima techniques still requires a high degree of talent and effort. We introduce a lightweight artificial intelligence system, Cambot, that can be used to assist in machinima production. Cambot takes a script as input and produces a cinematic visualization. Unlike other virtual cinematography systems, Cambot favors an offline algorithm coupled with an extensible library of specific modular and reusable facets of cinematic knowledge. One of the advantages of this approach to virtual cinematography is a tight coordination between the positions and movements of the camera and the actors

Toward Intelligent Support of Authoring Machinima Media Content: Story and Visualization

The Internet and the availability of authoring tools have enabled a greater community of media content creators, including nonexperts. However, while media authoring tools often make it technically feasible to generate, edit and share digital media artifacts, they do not guarantee that the works will be valuable or meaningful to the community at large. Therefore intelligent tools that support the authoring and creative processes are especially valuable. In this paper, we describe two intelligent support tools for the authoring and production of machinima. Machinima is a technique for producing computer-animated movies through the manipulation of computer game technologies. The first system we describe, ReQUEST, is an intelligent support tool for the authoring of plots. The second system, Cambot, produces machinima from a pre-authored script by manipulating virtual avatars and a virtual camera in a 3D graphical environment

Extracting Social Networks from Literary Fiction

We present a method for extracting social networks from literature, namely, nineteenth-century British novels and serials. We derive the networks from dialogue interactions, and thus our method depends on the ability to determine when two characters are in conversation. Our approach involves character name chunking, quoted speech attribution and conversation detection given the set of quotes. We extract features from the social networks and examine their correlation with one another, as well as with metadata such as the novel’s setting. Our results provide evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars. Instead, our results suggest an alternative explanation for differences in social networks

Columbia’s Newsblaster: New Features and Future Directions

Columbia's Newsblaster tracking and summarization system is a robust system that clusters news into events, categorizes events into broad topics and summarizes multiple articles on each event. Here we outline our most current work on tracking events over days, producing summaries that update a user on new information about an event, outlining the perspectives of news coming from different countries and clustering and summarizing non-English sources

Literature Lifts Up Computational Linguistics

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Mass Segregation in the Globular Cluster Palomar 5 and its Tidal Tails

We present the stellar main sequence luminosity function (LF) of the disrupted, low-mass, low-concentration globular cluster Palomar 5 and its well-defined tidal tails, which emanate from the cluster as a result of its tidal interaction with the Milky Way. The results of our deep (B ~ 24.5) wide-field photometry unequivocally indicate that preferentially fainter stars were removed from the cluster so that the LF of the cluster's main body exhibits a significant degree of flattening compared to other globular clusters. There is clear evidence of mass segregation, which is reflected in a radial variation of the LFs. The LF of the tidal tails is distinctly enhanced with faint, low-mass stars. Pal 5 exhibits a binary main sequence, and we estimate a photometric binary frequency of roughly 10%. Also the binaries show evidence of mass segregation with more massive binary systems being more strongly concentrated toward the cluster center.Comment: 14 pages, 12 figures, accepted for publication in the Astronomical Journa

DREADD activation of pedunculopontine cholinergic neurons reverses motor deficits and restores striatal dopamine signaling in parkinsonian rats

The brainstem-based pedunculopontine nucleus (PPN) traditionally associates with motor function, but undergoes extensive degeneration during Parkinson’s disease (PD), which correlates with axial motor deficits. PPN-Deep Brain Stimulation (DBS) can alleviate certain symptoms, but its mecha-nism(s) of action remains unknown. We previously characterized rats hemi-intranigrally injected with the proteasomal inhibitor lactacystin, as an accurate preclinical model of PD. Here we used a combination of chemogenetics with Positron Emission Tomography (PET) imaging for in vivo in-terrogation of discrete neural networks in this rat model of PD. Stimulation of excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed within PPN cholinergic neurons activated residual nigrostriatal dopaminergic neurons to produce pro-found motor recovery, which correlated with striatal dopamine efflux as well as restored dopamine receptor (DR) 1- and DR2-based medium spiny neuron (MSN) activity, as was ascertained with c-Fos-based immunohistochemistry and stereological cell counts. By revealing that the improved axi-al-related motor functions seen in PD patients receiving PPN-DBS may be due to stimulation of remaining PPN cholinergic neurons interacting with dopaminergic ones in both the Substantia Nigra pars compacta (SNpc) and the striatum, our data strongly favor the PPN cholinergic-midbrain dopaminergic connectome as mechanism for PPN-DBS’s therapeutic effects. These findings have implications for refining PPD-DBS as a promising treatment modality available to PD patients