Statistics

Statistics; by Freedman, Pisani, Purves, and Adhikari. Norton publishers
Introductory Statistics with R; by Dalgaard. Springer publishers.
Statistical Computing: An Introduction to Data Analysis using S-Plus; by Crawley. Wiley publishers
Statistical Process Control; by Grant, Leavenworth. McGraw-Hill publishers.
Statistical Methods for the Social Sciences; by Agresti, Finlay. Prentice-Hall publishers.
Methods of Social Research; by Baily. Free Press publishers.
Modern Applied Statistics with S-PLUS; by Venables, Ripley. Springer publishers.

Computational Narratology: Bibliography

Introductionary Text

Bremond, Claude (1970). “Morphology of the French Folktale.” Semiotica 2: 347–275.
Bringsjord, Selmer & David A. Ferrucci (2000). Artificial Intelligence and Literary Creativity: Inside the Mind of BRUTUS, a Storytelling Machine. Mahwah, NJ: Lawrence Erlbaum.
Campbell, Joseph ([1949] 1990). The Hero with a Thousand Faces. New York: Harper & Row.
Cavazza, M. & F. Charles (2005). “Dialogue Generation in Character-based Interactive Storytelling.” AAAI First Annual Artificial Intelligence and Interactive Digital Entertainment Conference, Marina del Rey, California [1].
Cavazza, M. & D. Pizzi (2006). “Narratology for Interactive Storytelling: A Critical Introduction.” S. Gobel, R. Malkewitz, & I. Iurgel (eds.), Technologies for Interactive Digital Storytelling and Entertainment. Third International Conference. Lecture Notes in Computer Science, 4326. Berlin: Springer.
Cheong, Y. G. (2007). A Computational Model of Narrative Generation for Suspense. PhD Thesis, Department of Computer Science, North Carolina State University.
Genette, Gérard ([1972] 1980). Narrative Discourse: An Essay in Method. Ithaca: Cornell UP.
Elson, David K., Nicholas Dames, & Kathleen R. McKeown (2010). “Extracting Social Networks from Literary Fiction.” Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL’2010), 138–47.
Freytag, Gustav (1900). Freytag's Technique of the drama : an exposition of dramatic composition and art. Translated by Elias J. MacEwan. Chicago: Scott, Foresman.
Gerrig, R. & D. Bernardo (1994) Readers as problem-solvers in the experience of suspense. Poetics 22: 459–72.
Gervás, Pablo, Birte Lönneker-Rodman, Jan Christoph Meister & Federico Peinado (2006). “Narrative Models: Narratology Meets Artificial Intelligence.” Proceedings of the LREC-06 workshop Toward Computational Models of Literary Analysis, Genoa, Italy.
Goguen, Joseph (2004). CSE 87C Winter 2004 Freshman Seminar on Computational Narratology. Department of Computer Science and Engineering, University of California, San Diego [2].
Goyal, Amit, Ellen Riloff & Hal Daumé III (2010). “Automatically Producing Plot Unit Representations for Narrative Text.” Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’2010), 77–86 [3].
Grasbon, D. & N. Braun (2001). “A Morphological Approach to Interactive Storytelling.” Proceedings of Artificial Intelligence and Interactive Entertainment, CAST '01, Living in Mixed Realities, Sankt Augustin, Germany, 337–40 [4].
Harrell, D. A. (2007). Theory and Technology for Computational Narrative. PhD Thesis, Departments of Computer Science and Cognitive Science, University of California, San Diego.
Lang, R. (2003). “A Declarative Model for Simple Narratives.” M. Mateas & P. Sengers (eds.), Narrative Intelligence. Amsterdam: John Benjamins.
Lehnert, W. G. (1981). “Plot Units: A Narrative Summarization Strategy.” W. G. Lehnert & M. H. Ringle (eds.), Strategies for Natural Language Processing. Hillsdale, NJ: Lawrence Erlbaum.

Mani, I., B. Wellner, M. Verhagen, C. M. Lee & J. Pustejovsky (2006). “Machine Learning of Temporal Relations.” Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 753–60.

Mani, I. (2010a). The Imagined Moment. Lincoln: U of Nebraska P.
Mani, I. (2010b). “Predicting Reader Response in Narrative.” 3rd Workshop on Intelligent Narrative Technologies. Foundations of Digital Games Conference, Monterey, CA, June 18, 2010.
Mateas, M. & A. Stern (2005). “Structuring Content in the Facade Interactive Drama Architecture.” Proceedings of Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2005), Marina del Rey.
Meister, Jan Christoph (2003). Computing Action. A Narratological Approach. Berlin: de Gruyter.
Meister, Jan Christoph (2011). “→ Narratology.” Paragraph 1–81. P. Hühn et al. (eds.), the living handbook of narratology. Hamburg: Hamburg UP.
Meister, Jan Christoph (2012). “Crowd sourcing “true meaning”. A collaborative markup approach to textual interpretation.” W. McCarty & M. Deegan (eds.), Festschrift for Harold Short. Surrey, U.K: Ashgate Publishers.
Montfort, Nick (2011). “Curveship's Automatic Narrative Variation.” Proceedings of the 6th International Conference on the Foundations of Digital Games (FDG '11), 211–18, Bordeaux, France.
Moretti, Franco (1999). Atlas of the European Novel, 1800–1900. London: Verso.
Peinado, Federico & Pablo Gervás (2006). “Evaluation of Automatic Generation of Basic Stories.” New Generation Computing 24: 289–302.
Pizzi, D. (2011). Emotional Planning for Character-based Interactive Storytelling. PhD Thesis, School of Computing, Teesside University, Middlesbrough.
Propp, Vladimir ([1928] 1968, 1988). Morphology of the Folktale. 2nd edn. Austin: U of Texas P.
Rumelhart, David E. (1980). “On Evaluating Story Grammars.” Cognitive Science 4: 313–16.
Salway, Andrew & David Herman (2008). “Digitized Corpora as Theory- Building Resource: New Foundations for Narrative Inquiry.” R. Page & B. Thomas (eds.), New Narratives: Theory and Practice. Lincoln: U of Nebraska P.
Schäfer, L., A. Stauber & B. Brokan (2004). “Storynet: An Educational Game for Social Skills.” *S. Göbel et al. (eds.), Technologies for Interactive Digital Storytelling and Entertainment, Second International Conference, TIDSE 2004, LNCS 3105. Berlin: Springer, 148–157.
Schank, Roger C. & Robert P. Abelson (1977). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum.
Šklovskij, Viktor B. (Shklovsky, Victor) ([1917] 1965). “Art as a Technique.” L. T. Lemon & M. *J. Reis (eds.), Russian Formalist Criticism. Lincoln: U of Nebraska P, 3–24.
Tomaševskij, Boris (Tomashevsky) ([1925] 1971). A Theory of Literature. Letchworth: Bradda Books.
Wilensky, Robert W. (1978). “Understanding Goal-based Stories.” Yale University Computer Science Research Report.

Web Resources

[AAAI Symposia http://www.aaai.org/Press/Reports/reports.php]
[Computational Linguistics for Literature http://sites.google.com/site/clfl2012/]
[ICIDS — Interactive Storytelling http://www.icids.org/]
[Intelligent Narrative Technologies http://www.aaai.org/Library/Workshops/ws11-18.php

Crowdsourcing: Emotional Speech

Laurel D. Riek, Maria F. O’Connor, and Peter Robinson,Guess What? A Game for Affective Annotation of Video Using Crowd Sourcing,
- Abstract. One of the most time consuming and laborious problems facing researchers in Affective Computing is annotation of data, particularly with the recent adoption of multimodal data. Other fields, such as Computer Vision, Language Processing and Information Retrieval have successfully used crowd sourcing (or human computation) games to label their data sets. Inspired by their work, we have developed a Facebook game called Guess What? for labeling multimodal, affective video data. This paper describes the game and an initial evaluation of it for social context labeling. In our experiment, 33 participants used the game to label 154 video/question pairs over the course of a few days, and their overall inter-rater reliability was good (Krippendorff’s α = .70). We believe this game will be a useful resource for other researchers and ultimately plan to make Guess What? open source and available to anyone who is interested.,
- Keywords: social context annotation, emotion annotation, video annotation,

human computation, crowd sourcing.

Alexey Tarasov, Charlie Cullen, Sarah Jane Delany, Using crowdsourcing for labelling emotional speech assets,
- The success of supervised learning approaches for the classification of emotion in speech depends highly on the quality of the training data. The manual annotation of emotion speech assets is the primary way of gathering training data for emotional speech recognition. This position paper proposes the use of crowdsourcing for the rating of emotion speech assets. Recent developments in learning from crowdsourcing over opportunities to determine accurate ratings for assets which have been annotated by large numbers of non-expert individuals. The challenges involved include identifying good annotators, determining consensus ratings and learning the bias of annotators.

John Snel (Dublin Institute of Technology, john.snel@student.dit.ie), Alexey Tarasov

(Dublin Institute of Technology, aleksejs.tarasovs@student.dit.ie), Charlie Cullen (Dublin Institute of Technology, charlie.cullen@dmc.dit.ie), Sarah Jane Delany (Dublin Institute of Technology, Sarahjane.Delany@dit.ie), A Crowdsourcing Approach to Labelling a Mood Induced Speech Corpus

- This paper demonstrates the use of crowdsourcing to accumulate ratings from na¨ıve listeners as a means to provide labels for a naturalistic emotional speech dataset. In order to do so, listening tasks are performed with a rating tool, which is delivered via the web. The rating requirements are based on the classical dimensions, activation and evaluation, presented to the participant as two discretised 5-point scales. Great emphasis is placed on the participant’s overall understanding of the task, and on the ease-of-use of the tool so that labelling accuracy is reinforced. The accumulation process is ongoing with a goal to supply the research community with a publicly available

speech corpus.

Carlo Strapparava, Rada Mihalcea, Alberto Battocchiy, A Parallel Corpus of Music and Lyrics Annotated with Emotions
- Abstract

In this paper, we introduce a novel parallel corpus of music and lyrics, annotated with emotions at line level. We first describe the corpus, consisting of 100 popular songs, each of them including a music component, provided in the MIDI format, as well as a lyrics component, made available as raw text. We then describe our work on enhancing this corpus with emotion annotations using crowdsourcing. We also present some initial experiments on emotion classification using the music and the lyrics representations of the songs, which lead to encouraging results, thus demonstrating the promise of using joint music-lyric models for song processing.

- Keywords: emotion recognition, multimodal processing, music analysis
Jonathon Read Erik Velldal Lilja Øvrelid, Labeling emotions in suicide notes:

Cost-sensitive learning with heterogeneous features

IZ STS:Bibliographie

Statistics

Computational Narratology: Bibliography

Introductionary Text

Further Reading

Web Resources

Crowdsourcing: Emotional Speech