Building large knowledge bases by mass collaboration
Proceedings of the 2nd international conference on Knowledge capture
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Morphological disambiguation by voting constraints
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Statistical morphological disambiguation for agglutinative languages
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Proceedings of the 3rd international conference on Knowledge capture
Collecting paraphrase corpora from volunteer contributors
Proceedings of the 3rd international conference on Knowledge capture
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving accessibility of the web with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning morphological disambiguation rules for Turkish
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Morphological Disambiguation of Turkish Text with Perceptron Algorithm
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
LAW '07 Proceedings of the Linguistic Annotation Workshop
Web-based annotation of anaphoric relations and lexical chains
LAW '07 Proceedings of the Linguistic Annotation Workshop
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In most of the natural language processing tasks, state-of-the-art systems usually rely on machine learning methods for building their mathematical models. Given that the majority of these systems employ supervised learning strategies, a corpus that is annotated for the problem area is essential. The current method for annotating a corpus is to hire several experts and make them annotate the corpus manually or by using a helper software. However, this method is costly and time-consuming. In this paper, we propose a novel method that aims to solve these problems. By employing a multiplayer collaborative game that is playable by ordinary people on the Internet, it seems possible to direct the covert labour force so that people can contribute by just playing a fun game. Through a game site which incorporates some functionality inherited from social networking sites, people are motivated to contribute to the annotation process by answering questions about the underlying morphological features of a target word. The experiments show that the 63.5% of the actual question types are successful based on a two-phase evaluation.