Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
On the explicit and implicit spatiotemporal architecture of narratives of personal experience
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
The use of granularity in rhetorical relation prediction
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We present the results of several machine learning tasks that exploit explicit spatial language to classify rhetorical relations and the spatial information of narrative events. Three corpora are annotated with figure and ground (granularity) relationships, mereotopologically classified verbs and prepositions, and frames of reference. For rhetorical relations, Naïve Bayesian models achieve 84.90% and 57.87% accuracy in classifying NARRATION and BACKGROUND/ELABORATION relations respectively (16% and 23% above baseline). For the spatial information of narrative events, K* models achieve 55.68% average accuracy (12% above baseline) for all spatial information types. This result is boosted to 71.85% (28% above baseline) when inertial spatial reference and text sequence information are considered. Overall, spatial information is shown to be central to narrative discourse structure and prediction tasks.