Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Knowledge management technology
IBM Systems Journal
Authoring branching storylines for training applications
ICLS '04 Proceedings of the 6th international conference on Learning sciences
Cue phrase classification using machine learning
Journal of Artificial Intelligence Research
Automated story capture from internet weblogs
Proceedings of the 4th international conference on Knowledge capture
A data-driven case-based reasoning approach to interactive storytelling
ICIDS'10 Proceedings of the Third joint conference on Interactive digital storytelling
Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
A Semantic Triplet Based Story Classifier
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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While storytelling has long been recognized as an important part of effective knowledge management in organizations, knowledge management technologies have generally not distinguished between stories and other types of discourse. In this paper we describe a new type of technological support for storytelling that involves automatically capturing the stories that people tell to each other in conversations. We describe our first attempt at constructing an automated story extraction system using statistical text classification and a simple voting scheme. We evaluate the performance of this system and demonstrate that useful levels of precision and recall can be obtained when analyzing transcripts of interviews, but that performance on speech recognition data is not above what can be expected by chance. This paper establishes the level of performance that can be obtained using a straightforward approach to story extraction, and outlines ways in which future systems can improve on these results and enable a wide range of knowledge socialization applications.