A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
Human Computation
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Open language learning for information extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
PATTY: a taxonomy of relational patterns with semantic types
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Automatic information extraction techniques for knowledge acquisition are known to produce noise, incomplete or incorrect facts from textual sources. Human computing offers a natural alternative to expand and complement the output of automated information extraction methods, thereby enabling us to build high-quality knowledge bases. However, relying solely on human inputs for extraction can be prohibitively expensive in practice. We demonstrate human computing games for knowledge acquisition that employ human computing to overcome the limitations in automated fact acquisition methods. We provide a combined approach that tightly integrates automated extraction techniques with human computing for effective gathering of facts. The methods we provide gather facts in the form of relationships between entities. The games we demonstrate are specifically designed to capture hard-to-extract relations between entities in narrative text -- a task that automated systems find challenging.