Drama annotation

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Drama annotation is the process of annotating the metadata of a drama. Given a drama expressed in some medium (text, video, audio, etc.), the process of metadata annotation identifies what are the elements that characterize the drama and annotates such elements in some metadata format. For example, in the sentence "Laertes and Polonius warn Ophelia to stay away from Hamlet." from the text Hamlet , the word "Laertes", which refers to a drama element, namely a character, will be annotated as "Char", taken from some set of metadata. This article addresses the drama annotation projects, with the sets of metadata and annotations proposed in the scientific literature, based markup languages and ontologies.

Contents

Drama across media and genres

Drama encompasses different media and languages, ranging from Greek tragedy and musical drama to action movies and video games: despite their huge differences, these examples share traits of the cultural construct that we recognise as drama. drama can be considered as a form of intangible cultural heritage, since it is characterised by an evolving nature, with form and function that change in time: for example, consider the difference between the Greek Tragedy Oedipus and the modernist play Six Characters in Search of an Author. The exponential spread of drama in contemporary culture has led Martin Esslin to forge the definition of “dramatic media", i.e. media that display characters performing live actions, such as theatre, cinema and videogames.

The discrete manifestations of drama are documented in different media, including text, score, video, audio, etc. The dramatic content underlying these manifestation, however, does not depend on the specific medium: take, for example, the Arden edition of the written drama of Hamlet [1] and Laurence Olivier's movie Hamlet, two examples of the drama heritage which share the same drama content despite the differences of the media support. The annotation of the content of media that convey dramatic content requires the use of an annotation schema expressed in a formal language, which makes the annotation comparable, and, possibly, machine readable.

The first attempts at attaching content metadata to media concerned text documents and were carried out by using markup languages, such as XML, which allow embedding content tags into the document text. With the advent of the Semantic Web project, [2] descriptive tools have evolved towards the use of ontologies, thanks to the languages and resources provided by the Semantic Web project. In particular, semantic annotation relies on the use of the Resource Description Framework language, specifically designed to described Web content of any type.

The semantic annotation of drama consists of representing the knowledge about drama in a machine-readable format to serve the task of annotating the dramatic content coherently across different media and languages, abstracting at the same time from the technicalities of signals and text encoding. The annotation of dramatic content across media and genres is a way to preserve, compare and study the nature of drama and of its manifestations. Moreover, the availability of content metadata about drama is a precondition for a range of generative tasks that range from automatising the generation of drama to supporting human creativity in this task.

Story and Drama Annotation through markup languages and linguistic schemata

Story annotation consists of annotating the content of narratives. In most cases, this effort is undertaken with the goal of constructing corpora of annotated narratives, or story corpora, finalised at the study of the relationship between the linguistic expression of the story in the narrative and its content. In the last decade, to a number of research initiatives especially oriented to the description of story and characters. For example, consider the Narrative Knowledge Representation Language (NKRL) [3] and the DramaBank project, [4] specifically oriented to the representation of story content in natural language texts.

The annotation of narrative texts has been prompted and influenced by two main lines of research. On the one side, the tradition of knowledge representation in AI has contributed the conceptual tools for describing the content of stories, with languages that span from scripts [5] [Schank 1975] to frames. [6] The linguistic counterpart of this line of research has resulted in resources situated at the lexical-semantic level (such as FrameNet [7] and at the interface between syntax and semantics (such as PropBank, which offer tools for representing the connection between the expression of the narrative through the text and the narrative content itself). For example, the Story Workbench tool [8] encompasses a layered annotation scheme, which uses these resources for the multi-layer annotation of narratives. On the other side, the annotation of narratives has benefited from the trend, established during the last three decades, [9] of representing the content of documents in a machine-readable form. With the advent of markup languages such as Text Encoding Initiative (TEI) for encoding text in digital form and annotating their structure, the use of markup has soon become the standard in text annotation projects.

In particular, projects such as Narrative Knowledge Representation Language (NKRL) [10] leveraged the use of markup languages for the representation of the narrative content of text, revamping the use of frames into the emerging scenario of media indexing and retrieval. More recently, as part of the more general effort of constructing resources for the automation of language processing and generation, Elson has proposed a template based language for describing the narrative content of text documents, with the goal of creating a corpus of annotated narrative texts, called DramaBank project. [11] In recent years, the annotation of narrative text has evolved towards minimal schemata targeted at grasping the regularities of written and oral narratives at the discourse level. [12] These initiatives, however, rooted in narrative theories, tend to focus on the realization of narratives though a specific medium, i.e., text, leaving behind the universal elements of dramatic narration that go behind the expressive characteristics of each medium.

Story and drama annotation through computational ontologies

Mostly oriented to the indexing and retrieval of media, ontologies, and vocabularies have appeared that support the representation of the content of media according to a shared semantics, available across the Web. In particular, semantic resources such as VERL (the Video Event Representation Language [13] and LODE (Linked Open Description of Events) [14] provide structured description of events that can be applied to the description of incidents in stories. In the paradigm of Linked Data, these resources become the infrastructure for content-based applications in the field of media: for example, consider the project EventMedia, [15] where different vocabularies have been aligned with automatic methods to create an illustrated catalog of media representing events.

A media-independent model of story is provided by the OntoMedia ontology, exploited across different projects (such as the Contextus Project [16] to annotate the narrative content of different media objects, ranging from written literature to comics and TV fiction. This project encompasses some concepts, such the notion of character, that are relevant for the description of drama, but, being mainly focused on the representation of events and the order in which they are exposed in media for cross-media comparison, it lacks the capability of representing the core notions of drama. In the field of cultural heritage dissemination, the StorySpace ontology, [17] an ontology of story, supports museum curators in linking the content of artworks through stories, with the ultimate goal of enabling the generation of user tailored content retrieval. [18]

A line of research has tackled the use of logical representations to describe stories, with the goal of creating generative systems that leverage the axiomatization of narrative structures. [19] Finally, some scholars have created representational tools for specific narrative theories, ranging from literary structuralism [20] to scriptwriting practices [21]

Related Research Articles

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.

MPEG-7 is a multimedia content description standard. It was standardized in ISO/IEC 15938. This description will be associated with the content itself, to allow fast and efficient searching for material that is of interest to the user. MPEG-7 is formally called Multimedia Content Description Interface. Thus, it is not a standard which deals with the actual encoding of moving pictures and audio, like MPEG-1, MPEG-2 and MPEG-4. It uses XML to store metadata, and can be attached to timecode in order to tag particular events, or synchronise lyrics to a song, for example.

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

Web annotation can refer to online annotations of web resources such as web pages or parts of them, or a set of W3C standards developed for this purpose. The term can also refer to the creations of annotations on the World Wide Web and it has been used in this sense for the annotation tool INCEpTION, formerly WebAnno. This is a general feature of several tools for annotation in natural language processing or in the philologies.

An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation.

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving".

RDFa or Resource Description Framework in Attributes is a W3C Recommendation that adds a set of attribute-level extensions to HTML, XHTML and various XML-based document types for embedding rich metadata within Web documents. The Resource Description Framework (RDF) data-model mapping enables its use for embedding RDF subject-predicate-object expressions within XHTML documents. It also enables the extraction of RDF model triples by compliant user agents.

<span class="mw-page-title-main">Semantic technology</span> Technology to help machines understand data

The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF and OWL. These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources.

<span class="mw-page-title-main">Systems Biology Ontology</span>

The Systems Biology Ontology (SBO) is a set of controlled, relational vocabularies of terms commonly used in systems biology, and in particular in computational modeling.

Semantic publishing on the Web, or semantic web publishing, refers to publishing information on the web as documents accompanied by semantic markup. Semantic publication provides a way for computers to understand the structure and even the meaning of the published information, making information search and data integration more efficient.

Linguistic categories include

<span class="mw-page-title-main">DBpedia</span> Online database project

DBpedia is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets.

Amit Sheth is a computer scientist at University of South Carolina in Columbia, South Carolina. He is the founding Director of the Artificial Intelligence Institute, and a Professor of Computer Science and Engineering. From 2007 to June 2019, he was the Lexis Nexis Ohio Eminent Scholar, director of the Ohio Center of Excellence in Knowledge-enabled Computing, and a Professor of Computer Science at Wright State University. Sheth's work has been cited by over 48,800 publications. He has an h-index of 106, which puts him among the top 100 computer scientists with the highest h-index. Prior to founding the Kno.e.sis Center, he served as the director of the Large Scale Distributed Information Systems Lab at the University of Georgia in Athens, Georgia.

<span class="mw-page-title-main">Ontology engineering</span> Field that studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

<span class="mw-page-title-main">Text annotation</span> Adding a note or gloss to a text

Text annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links. Text annotations can include notes written for a reader's private purposes, as well as shared annotations written for the purposes of collaborative writing and editing, commentary, or social reading and sharing. In some fields, text annotation is comparable to metadata insofar as it is added post hoc and provides information about a text without fundamentally altering that original text. Text annotations are sometimes referred to as marginalia, though some reserve this term specifically for hand-written notes made in the margins of books or manuscripts. Annotations have been found to be useful and help to develop knowledge of English literature.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

In markup languages and the digital humanities, overlap occurs when a document has two or more structures that interact in a non-hierarchical manner. A document with overlapping markup cannot be represented as a tree. This is also known as concurrent markup. Overlap happens, for instance, in poetry, where there may be a metrical structure of feet and lines; a linguistic structure of sentences and quotations; and a physical structure of volumes and pages and editorial annotations.

In natural language processing, linguistics, and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing, and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data Cloud was conceived and is being maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects, and infrastructure efforts since then.

In linguistics and language technology, a language resource is a "[composition] of linguistic material used in the construction, improvement and/or evaluation of language processing applications, (...) in language and language-mediated research studies and applications."

References

  1. ↑ Shakespeare, William; edited by Harold Jenkins (1997). Hamlet (Repr. by Nelson. ed.). Walton-on-Thames: Thomas Nelson. ISBN   978-0174434696.{{cite book}}: |first1= has generic name (help)CS1 maint: multiple names: authors list (link)
  2. ↑ Berners-Lee, Tim; Hendler, James; Lassila, Ora (1 January 2001). "The Semantic Web". Scientific American. 284 (5): 34–43. Bibcode:2001SciAm.284e..34B. doi:10.1038/scientificamerican0501-34. Archived from the original on 6 September 2018. Retrieved 12 October 2016.
  3. ↑ ZARRI, GIAN PIERO (September 1997). "NKRL, a knowledge representation tool for encoding the 'meaning' of complex narrative texts". Natural Language Engineering. 3 (2): 231–253. CiteSeerX   10.1.1.519.7577 . doi:10.1017/S1351324997001794. S2CID   29721071.
  4. ↑ Elson, David K. "DramaBank: Annotating Agency in Narrative Discourse" (PDF): 2813–2819.{{cite journal}}: Cite journal requires |journal= (help)
  5. ↑ Schank, Roger C.; Abelson, Robert P. (1 January 1975). "Scripts, Plans, and Knowledge". Proceedings of the 4th International Joint Conference on Artificial Intelligence - Volume 1. Ijcai'75: 151–157.
  6. ↑ Minsky, Marvin (1 January 1975). "Minsky's Frame System Theory". Proceedings of the 1975 Workshop on Theoretical Issues in Natural Language Processing. Tinlap '75: 104–116. doi: 10.3115/980190.980222 .
  7. ↑ Baker, Collin F.; Fillmore, Charles J.; Lowe, John B. (1 January 1998). "The Berkeley FrameNet Project". Proceedings of the 36th annual meeting on Association for Computational Linguistics -. Vol. 1. pp. 86–90. doi: 10.3115/980845.980860 .{{cite book}}: |journal= ignored (help)
  8. ↑ Finlayson, Mark Alan. "The Story Workbench: An Extensible Semi-Automatic Text Annotation Tool": 21–24.{{cite journal}}: Cite journal requires |journal= (help)
  9. ↑ Coombs, James H.; Renear, Allen H.; DeRose, Steven J. (1 November 1987). "Markup Systems and the Future of Scholarly Text Processing". Commun. ACM. 30 (11): 933–947. CiteSeerX   10.1.1.515.5618 . doi:10.1145/32206.32209. ISSN   0001-0782. S2CID   59941802.
  10. ↑ ZARRI, GIAN PIERO (September 1997). "NKRL, a knowledge representation tool for encoding the 'meaning' of complex narrative texts". Natural Language Engineering. 3 (2): 231–253. CiteSeerX   10.1.1.519.7577 . doi:10.1017/S1351324997001794. S2CID   29721071.
  11. ↑ http://lrec.elra.info/proceedings/lrec2012/pdf/866_Paper.pdf [ bare URL PDF ]
  12. ↑ Rahimtoroghi, Elahe; Corcoran, Thomas; Swanson, Reid; Walker, Marilyn A.; Sagae, Kenji; Gordon, Andrew (23 October 2014). "Minimal Narrative Annotation Schemes and Their Applications".{{cite journal}}: Cite journal requires |journal= (help)
  13. ↑ Francois, A.R.J.; Nevatia, R.; Hobbs, J.; Bolles, R.C. (October 2005). "VERL: An Ontology Framework for Representing and Annotating Video Events". IEEE MultiMedia. 12 (4): 76–86. doi:10.1109/MMUL.2005.87. S2CID   8772560.
  14. ↑ Shaw, Ryan; Troncy, Raphaël; Hardman, Lynda (6 December 2009). "LODE: Linking Open Descriptions of Events". The Semantic Web. Lecture Notes in Computer Science. Vol. 5926. pp. 153–167. CiteSeerX   10.1.1.154.5031 . doi:10.1007/978-3-642-10871-6_11. ISBN   978-3-642-10870-9. S2CID   14327857.
  15. ↑ Khrouf, Houda; Troncy, Raphael (2016). "EventMedia: a LOD Dataset of Events Illustrated with Media". Semantic Web. 7 (2): 193–199. doi:10.3233/SW-150184.
  16. ↑ "Jey Biddulph".
  17. ↑ Wolff, Annika; Mulholland, Paul; Collins, Trevor (1 January 2012). "Storyspace". Proceedings of the 23rd ACM conference on Hypertext and social media. pp. 89–98. doi:10.1145/2309996.2310012. ISBN   9781450313353. S2CID   3340126.
  18. ↑ Mulholland, P.; Collins, T. (1 September 2002). "Using digital narratives to support the collaborative learning and exploration of cultural heritage". Proceedings. 13th International Workshop on Database and Expert Systems Applications. 13th International Workshop on Database and Expert Systems Applications, 2002. Proceedings. pp. 527–531. CiteSeerX   10.1.1.8.7977 . doi:10.1109/DEXA.2002.1045951. ISBN   978-0-7695-1668-4.
  19. ↑ Martens, Chris; Bosser, Anne-Gwenn; Ferreira, João F.; Cavazza, Marc (15 September 2013). "Linear Logic Programming for Narrative Generation". Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science. Vol. 8148. pp. 427–432. doi:10.1007/978-3-642-40564-8_42. hdl:10149/312346. ISBN   978-3-642-40563-1.
  20. ↑ Peinado, Federico; Gervás, Pablo; Díaz-Agudo, Belén (2004). "A description logic ontology for fairy tale generation". LREC Workshop on Language Resources for Linguistic Creativity. 4: 56–61.
  21. ↑ Szilas, Nicolas (11 February 2016). "Modeling and Representing Dramatic Situations as Paradoxical Structures". Digital Scholarship in the Humanities. 32 (2): 403–422. doi:10.1093/llc/fqv071. ISSN   2055-7671.
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