A philosophical basis for knowledge acquisition
Knowledge Acquisition
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Inferring temporal ordering of events in news
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
RDRCE: combining machine learning and knowledge acquisition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Ripple down rules for part-of-speech tagging
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
RDR-based open IE for the web document
Proceedings of the sixth international conference on Knowledge capture
Improving open information extraction for informal web documents with ripple-down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Improving the performance of a named entity recognition system with knowledge acquisition
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
| Hi-index | 0.00 |
Machine learning approaches in natural language processing often require a large annotated corpus. We present a complementary approach that utilizes expert knowledge to overcome the scarceness of annotated data. In our framework KAFTIE, the expert could easily create a large number of rules in a systematic manner without the need of a knowledge engineer. Using KAFTIE, a knowledge base was built based on a small data set that outperforms machine learning algorithms trained on a much bigger data set for the task of recognizing temporal relations. Furthermore, our knowledge acquisition approach could be used in synergy with machine learning algorithms to both increase the performance of the machine learning algorithms and to reduce the expert's knowledge acquisition effort.