Building natural language generation systems
Building natural language generation systems
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic Short Story Generator Based on Autonomous Agents
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Makebelieve: using commonsense knowledge to generate stories
Eighteenth national conference on Artificial intelligence
A fast and portable realizer for text generation systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Two-level, many-paths generation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Comlex Syntax: building a computational lexicon
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Capturing the interaction between aggregation and text planning in two generation systems
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Planning algorithms for interactive storytelling
Computers in Entertainment (CIE) - Interactive entertainment
Modeling local coherence: An entity-based approach
Computational Linguistics
Interactive Storytelling with Literary Feelings
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
TALE-SPIN, an interactive program that writes stories
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Learning to tell tales: a data-driven approach to story generation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Unsupervised learning of narrative schemas and their participants
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Extending the entity grid with entity-specific features
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Skip n-grams and ranking functions for predicting script events
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Character-based kernels for novelistic plot structure
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Story characterization using interactive evolution in a multi-agent system
EvoMUSART'13 Proceedings of the Second international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
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In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children's stories show that our system outperforms previous data-driven approaches.