Anthony Davis


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Narrative Schema Stability in News Text
Dan Simonson | Anthony Davis
Proceedings of the 27th International Conference on Computational Linguistics

We investigate the stability of narrative schemas (Chambers and Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson and Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of “more of the same.”


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NASTEA: Investigating Narrative Schemas through Annotated Entities
Dan Simonson | Anthony Davis
Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)


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Interactions between Narrative Schemas and Document Categories
Dan Simonson | Anthony Davis
Proceedings of the First Workshop on Computing News Storylines


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A System for Predicting ICD-10-PCS Codes from Electronic Health Records
Michael Subotin | Anthony Davis
Proceedings of BioNLP 2014


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Contextually-Mediated Semantic Similarity Graphs for Topic Segmentation
Geetu Ambwani | Anthony Davis
Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing