Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
SystemT: an algebraic approach to declarative information extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Efficient statement identification for automatic market forecasting
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Building a generic debugger for information extraction pipelines
Proceedings of the 20th ACM international conference on Information and knowledge management
Constructing efficient information extraction pipelines
Proceedings of the 20th ACM international conference on Information and knowledge management
Web-based open-domain information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Adaptive document analysis with planning
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Overview of Genia event task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Thinking outside the box for natural language processing
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
ICWS '12 Proceedings of the 2012 IEEE 19th International Conference on Web Services
Information extraction as a filtering task
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Many annotation tasks in computational linguistics are tackled with manually constructed pipelines of algorithms. In real-time tasks where information needs are stated and addressed ad-hoc, however, manual construction is infeasible. This paper presents an artificial intelligence approach to automatically construct annotation pipelines for given information needs and quality prioritizations. Based on an abstract ontological model, we use partial order planning to select a pipeline's algorithms and informed search to obtain an efficient pipeline schedule. We realized the approach as an expert system on top of Apache UIMA, which offers evidence that pipelines can be constructed ad-hoc in near-zero time.