Linguistic Issues in Language Technology – LiLT

Since the 18th century, the novel has been one of the defining forms of English writing, a mainstay of popular entertainment and academic criticism. Despite its importance, however, there are few computational studies of the large-scale structure of novels—and many popular repre- sentations for discourse modeling do not work very well for novelistic texts. This paper describes a high-level representation of plot structure which tracks the frequency of mentions of different characters, topics and emotional words over time. The representation can distinguish with high accuracy between real novels and artificially permuted surrogates; characters are important for eliminating random permutations, while topics are effective at distinguishing beginnings from ends.

[1]  May Pian-Smith,et al.  A Writer's Toolkit , 2012 .

[2]  Eugene Charniak,et al.  Unsupervised Learning of Name Structure From Coreference Data , 2001, NAACL.

[3]  David Bamman,et al.  A Bayesian Mixed Effects Model of Literary Character , 2014, ACL.

[4]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[5]  Byron C. Wallace.  Multiple Narrative Disentanglement: Unraveling Infinite Jest , 2012, NAACL.

[6]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[7]  Kathleen McKeown,et al.  Extracting Social Networks from Literary Fiction , 2010, ACL.

[8]  Mirella Lapata,et al.  Modeling Local Coherence: An Entity-Based Approach , 2005, ACL.

[9]  Claire Cardie,et al.  Compositional Matrix-Space Models for Sentiment Analysis , 2011, EMNLP.

[10]  Mirella Lapata,et al.  Plot Induction and Evolutionary Search for Story Generation , 2010, ACL.

[11]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[12]  Saif Mohammad,et al.  From once upon a time to happily ever after: Tracking emotions in mail and books , 2012, Decis. Support Syst..

[13]  Matt Post,et al.  Judging Grammaticality with Tree Substitution Grammar Derivations , 2011, ACL.

[14]  Thorsten Joachims,et al.  Learning structural SVMs with latent variables , 2009, ICML '09.

[15]  Micha Elsner,et al.  A Unified Local and Global Model for Discourse Coherence , 2007, NAACL.

[16]  Mark A. Finlayson.  DERIVING NARRATIVE MORPHOLOGIES VIA ANALOGICAL STORY MERGING , 2009 .

[17]  Narrative as Rhetoric , 2005 .

[18]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[19]  Chris Mellish,et al.  Evaluating Centering-Based Metrics of Coherence , 2004, ACL.

[20]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[21]  Micha Elsner,et al.  Character-based kernels for novelistic plot structure , 2012, EACL.

[22]  Ellen Riloff,et al.  Automatically Producing Plot Unit Representations for Narrative Text , 2010, EMNLP.

[23]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[24]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[25]  Caroline Sporleder,et al.  Structure-based Clustering of Novels , 2014, CLfL@EACL.

[26]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[27]  Anna Kazantseva,et al.  Summarizing Short Stories , 2010, CL.

[28]  David Haussler,et al.  Convolution kernels on discrete structures , 1999 .

[29]  Brian O'Neill,et al.  Toward a Computational Framework of Suspense and Dramatic Arc , 2011, ACII.

[30]  Jean-Philippe Tarel,et al.  Non-Mercer Kernels for SVM Object Recognition , 2004, BMVC.

[31]  Walt Detmar Meurers,et al.  Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text , 2010, HLT-NAACL 2010.

[32]  Graeme Hirst,et al.  Extending the Entity-based Coherence Model with Multiple Ranks , 2012, EACL.

[33]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[34]  Lise Getoor,et al.  Relational clustering for multi-type entity resolution , 2005, MRDM '05.

[35]  Roger C. Schank,et al.  Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .

[36]  Mirella Lapata,et al.  Learning to Tell Tales: A Data-driven Approach to Story Generation , 2009, ACL.

[37]  Wendy G. Lehnert,et al.  Plot Units and Narrative Summarization , 1981, Cogn. Sci..

[38]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[39]  Mark O. Riedl,et al.  Crowdsourcing Narrative Intelligence , 2012 .

[40]  Saif Mohammad,et al.  From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales , 2011, LaTeCH@ACL.

[41]  Peter D. Turney,et al.  Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon , 2010, HLT-NAACL 2010.

[42]  Cecilia Ovesdotter Alm,et al.  Emotional Sequencing and Development in Fairy Tales , 2005, ACII.

[43]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[44]  Ben Taskar,et al.  Alignment by Agreement , 2006, NAACL.

[45]  James R. Curran,et al.  A Sequence Labelling Approach to Quote Attribution , 2012, EMNLP.

[46]  Marie-Laure Ryan.  Possible Worlds, Artificial Intelligence, and Narrative Theory , 1992 .

[47]  Nathanael Chambers,et al.  Unsupervised Learning of Narrative Schemas and their Participants , 2009, ACL.

[48]  Kathleen McKeown,et al.  Building a Bank of Semantically Encoded Narratives , 2010, LREC.

[49]  Erik B. Sudderth,et al.  The Doubly Correlated Nonparametric Topic Model , 2011, NIPS.

[50]  Brendan T. O'Connor,et al.  Learning Latent Personas of Film Characters , 2013, ACL.

[51]  S. V. N. Vishwanathan,et al.  Graph kernels , 2007 .

[52]  David M. Blei,et al.  Bayesian Checking for Topic Models , 2011, EMNLP.

[53]  Vladimir Propp,et al.  Morphology of the folktale , 1959 .

[54]  Hermann Ney,et al.  Symmetric Word Alignments for Statistical Machine Translation , 2004, COLING.