Modeling Reportable Events as Turning Points in Narrative

We present novel experiments in modeling the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to automatically identify narratives. Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives. We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and selftraining experiments that achieve significant improvements over the baselines at the task of identifying MREs.

[1]  Stuart Adam Battersby,et al.  Experimenting with Distant Supervision for Emotion Classification , 2012, EACL.

[2]  Andrew McCallum,et al.  Collective Cross-Document Relation Extraction Without Labelled Data , 2010, EMNLP.

[3]  Ani Nenkova,et al.  What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain , 2013, TACL.

[4]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[5]  Weiwei Guo,et al.  Modeling Sentences in the Latent Space , 2012, ACL.

[6]  Fabian Flöck,et al.  Evolution of reddit: from the front page of the internet to a self-referential community? , 2014, WWW.

[7]  Kathy McKeown,et al.  Towards Automatic Detection of Narrative Structure , 2014, LREC.

[8]  Livia Polanyi,et al.  Telling the American Story: A Structural and Cultural Analysis of Conversational Storytelling , 1985 .

[9]  Nancy Ide,et al.  Distant Supervision for Emotion Classification with Discrete Binary Values , 2013, CICLing.

[10]  Kenji Sagae.  Self-Training without Reranking for Parser Domain Adaptation and Its Impact on Semantic Role Labeling , 2010 .

[11]  Hans Uszkoreit,et al.  Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web , 2012, International Semantic Web Conference.

[12]  Cynthia Whissell,et al.  THE DICTIONARY OF AFFECT IN LANGUAGE , 1989 .

[13]  Alessandro Moschitti,et al.  End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories , 2011, ACL.

[14]  W. Labov,et al.  Narrative analysis: Oral versions of personal experience. , 1997 .

[15]  Ari Rappoport,et al.  Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets , 2007, ACL.

[16]  C. W. Hughes.  Emotion: Theory, Research and Experience , 1982 .

[17]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[18]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[19]  Eric Gilbert,et al.  Widespread underprovision on Reddit , 2013, CSCW.

[20]  Le Zhao,et al.  Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction , 2013, ACL.

[21]  Elahe Rahimtoroghi,et al.  Identifying Narrative Clause Types in Personal Stories , 2014, SIGDIAL Conference.

[22]  W. Labov.  Some Further Steps in Narrative Analysis , 1997 .

[23]  Eugene Charniak,et al.  Effective Self-Training for Parsing , 2006, NAACL.

[24]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[25]  Jiawei Han,et al.  An exploration of discussion threads in social news sites: A case study of the Reddit community , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[26]  Luke S. Zettlemoyer,et al.  Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations , 2011, ACL.

[27]  Ani Nenkova,et al.  Inducing Lexical Style Properties for Paraphrase and Genre Differentiation , 2015, NAACL.

[28]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[29]  Rada Mihalcea,et al.  Co-training and Self-training for Word Sense Disambiguation , 2004, CoNLL.

[30]  Gerald Prince,et al.  A Grammar of Stories: An Introduction , 1974 .

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

[32]  Reid Swanson,et al.  Automated story capture from internet weblogs , 2007, K-CAP '07.

[33]  Fadi Biadsy,et al.  Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams , 2009, EACL.

[34]  R. Swanson,et al.  Identifying Personal Stories in Millions of Weblog Entries , 2009, ICWSM 2009.

[35]  Livia Polanyi Bowditch.  Why the Whats are When: Mutually Contextualizing Realms of Narrative , 1976 .

[36]  Ralph Grishman,et al.  Distant Supervision for Relation Extraction with an Incomplete Knowledge Base , 2013, NAACL.

[37]  Mary P. Harper,et al.  Self-Training PCFG Grammars with Latent Annotations Across Languages , 2009, EMNLP.

[38]  William Labov.  The Language of Life and Death: S. T. Bindoff: “The death of Elizabeth” , 2013 .

[39]  Morton Botel,et al.  A Formula for Measuring Syntactic Complexity; A Directional Effort. , 1972 .

[40]  Elahe Rahimtoroghi,et al.  Evaluation, Orientation, and Action in Interactive StoryTelling , 2013, Intelligent Narrative Technologies.