schema narratif in english


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PDF Unsupervised Learning of Narrative Schemas and their Participants

We describe an unsupervised system for learn-ing narrative schemas coherent sequences or sets of events (arrested(POLICESUSPECT) convicted( JUDGE SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = fjudge jury courtg POLICE = fpolice agent authoritiesg) Unlike most previous work in event struct

PDF The Narrative Arc

Voici un tableau décrivant les éléments du « narrative arc » c‘est-à-dire le schéma narratif en français Elements of the Narrative Arc and their correspondences with the “Schéma narratif” Element Definition Correspondent element of the “schéma narratif” Exposition This is the introduction of the story helping the

PDF A Database of Narrative Schemas

Narrative schemas contain sets of related events (edit and publish) a temporal ordering of the events (edit before publish) and the semantic roles of the participants (authors publish books) This type of world knowledge was central to early research in natural language understanding

  • What is schema on read?

    The raw data is then accessed using a schema on read (ie, applying structure to the data as it is read) approach, also known as ELT (extract-load-transform), in which the business logic and data transformation is applied when accessing the data, and is often done on an ad-hoc basis.

  • What is a narrative arc?

    Voici un tableau décrivant les éléments du « narrative arc », c‘est-à-dire le schéma narratif en français. This is the introduction of the story, helping the reader understand WHERE and WHEN we are, WHO is involved and WHAT is going on. This is where the conflicts appear and the tension rises.

  • How does a story end in a schéma narratif?

    Elements of the story are clarified and the major conflict gives way to the Resolution. Dans le schéma narratif, cet élément est compris dans le dénouement. This is how the story ends. The story has found a certain balance, or calm, whether it has a “good” or a “bad” ending.

Abstract

We describe an unsupervised system for learn-ing narrative schemas, coherent sequences or sets of events (arrested(POLICE,SUSPECT), convicted( JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = fjudge, jury, courtg, POLICE = fpolice, agent, authoritiesg). Unlike most previous work in event struct

2 Background

This paper addresses two areas of work in event semantics, narrative event chains and semantic role labeling. We begin by highlighting areas in both that can mutually inform each other through a narrative schema model. cs.stanford.edu

2.1.1 The Case for Arguments

The Chambers and Jurafsky (2008) narrative chains do not specify what type of argument fills the role of protagonist. Chain learning and clus-tering is based only on the frequency with which two verbs share arguments, ignoring any features of the arguments themselves. Take this example of an actual chain from an article in our training data. Given

2.1.2 The Case for Joint Chains

The second problem with narrative chains is that they make judgments only between protagonist ar-guments, one slot per event. All entities and slots in the space of events should be jointly considered when making event relatedness decisions. As an illustration, consider the verb arrest. Which verb is more related, convict or capture? A narrative ch

3 Narrative Schemas

The next sections introduce typed narrative chains and chain merging, extensions that allow us to jointly learn argument roles with event structure. cs.stanford.edu

3.1 Typed Narrative Chains

The first step in describing a narrative schema is to extend the definition of a narrative chain to include argument types. We now constrain the protagonist to be of a certain type or role. A Typed Narrative Chain is a partially ordered set of event slots that share an argument, but now the shared argument is a role defined by being a member of a s

3.2 Learning Argument Types

As mentioned above, narrative chains are learned by parsing the text, resolving coreference, and ex-tracting chains of events that share participants. In our new model, argument types are learned simul-taneously with narrative chains by finding salient words that represent coreferential arguments. We record counts of arguments that are observed wit

3.4 Narrative Schema: Multiple Chains

Whereas a narrative chain is a set of event slots, a Narrative Schema is a set of typed narrative chains. A schema thus models all actors in a set of events. If (push X) is in one chain, (Y push) is in another. This allows us to model a document’s entire narrative, not just one main actor. cs.stanford.edu

3.4.2 Learning Narrative Schemas

Previous work on narrative chains focused on re-latedness scores between pairs of verb arguments (event slots). The clustering step which built chains depended on these pairwise scores. Narra-tive schemas use a generalization of the entire verb with all of its arguments. A joint decision can be made such that a verb is added to a schema if both its

7 Discussion

Our significant improvement in the cloze evalua-tion shows that even though narrative cloze does not evaluate argument types, jointly modeling the arguments with events improves event cluster-ing. Likewise, the FrameNet comparison suggests that modeling related events helps argument learn-ing. The tasks mutually inform each other. Our argument lear

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Quels sont les 5 schéma narratif ?

La situation initiale. L'élément perturbateur, le déclenchement. Les péripéties. Le dénouement.










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