Computational Models of Narrative: Review of the Workshop

By Finlayson, Mark A.; Richards, Whitman et al. | AI Magazine, Summer 2010 | Go to article overview

Computational Models of Narrative: Review of the Workshop


Finlayson, Mark A., Richards, Whitman, Winston, Patrick H., AI Magazine


Narratives are ubiquitous in human experience. We use them to entertain, communicate, convince, and explain. One workshop participant noted that "as far as I know, every society in the world has stories, which suggests they have a psychological basis, that stories do something for you." To truly understand and explain human intelligence, reasoning, and beliefs, we need to understand why narrative is universal and explain the function it serves.

Computational modeling is a natural method for investigating narrative. As a complex cognitive phenomenon, narrative touches on many areas that have traditionally been of interest to artificial intelligence researchers: its different facets draw on our capacities for natural language understanding and generation, commonsense reasoning, analogical reasoning, planning, physical perception (through imagination), and social cognition. Successful modeling will undoubtedly require researchers from these many perspectives and more, using a multitude of different techniques from the AI toolkit, ranging from, for example, detailed symbolic knowledge representation to large-scale statistical analyses. The relevance of AI to narrative, and vice versa, is compelling.

The Computational Models of Narrative workshop (1) had three main objectives: (1) to understand the scope and dimensions of narrative models, identifying gaps and next steps, (2) to evaluate the state of the art, and (3) to begin to build a community focused on computational narrative. The interdisciplinary group of 22 participants (see figure 1) included computer scientists, psychologists, linguists, media developers, philosophers, and storytellers. Ten speakers were selected to represent a range of views, and their presentations were organized into four groups, each followed by an extensive discussion moderated by a panel. The day after the presentations, there was a lively, morning-long extended discussion. The meeting's audio was captured and later analyzed in depth. A detailed summary of the group's conclusions at the workshop appears elsewhere (Richards, Finlayson, and Winston 2009), together with recommendations for future initiatives. (2) Regarding models of narrative, the main findings were: (1) a three-level organization of narrative representations unifies work in the area, (2) the area suffers from a deficit of investigation at the highest, most abstract level aimed at the "meaning" of the narrative, and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank (Marcus, Marcinkiewicz, and Santorini 1993).

A Three-Level Organization

Computational modeling requires a precise statement of the problem (or problems) to be solved. Thus, an obvious first step is to understand how narrative should be represented.

There were three common denominators among the representations presented at the workshop: (1) narratives have to do with sequences of events, (2) narratives have hierarchical structure, and (3) they are grounded in a commonsense knowledge of the world. Similarly, it was uncontroversial that narratives can be told from multiple points of view, and that all four of these characteristics were independent of whether or not a narrative was told with words. (3)

After analysis of the presentations and discussions, it became clear that all the representations considered at the workshop were subsumed within a three-level structure. The heavily investigated middle level stressed event sequences that were built on the classic logical-predicate-like representations introduced in artificial intelligence in its earliest days, exemplified by instances such as KISS (JOHN, MARY) and CAUSE(SHOOT, DIE).

Below the middle level were representations that examined the detailed structure of the narratives in question. There was quite a bit of work at this detail level, such as commonsense reasoning (Mueller 2007), discourse structures (Asher and Lascarodes 2003), argument-support hierarchies (Bex, Prakken, and Verheij 2007), or plan graphs (Young 2007). …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Computational Models of Narrative: Review of the Workshop
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.