PackPlay: Mining Semantic Data in Collaborative Games
Nathan Green, Paul Breimyer, Vinay Kumar, Nagiza F. Samatova · 2010
Building training data is labor-intensive and presents a major obstacle to advancing machine learning technologies such as machine translators, named entity recognizers (NER), part-of-speech taggers, etc. Training data are often specialized for a particular language or Natural Language Processing (NLP) task. Knowledge captured by a specific set of training data is not easily transferable, even to the same NLP task in another language. Emerging technologies, such as social networks and serious games, offer a unique opportunity to change how we construct training data. While collaborative games have been used in information retrieval, it is an open issue whether users can contribute accurate annotations in a collaborative game context for a problem that requires an exact answer, such as games that would create named entity recognition training data. We present PackPlay, a collaborative game framework that empirically shows players’ ability to mimic annotation accuracy and thoroughness seen in gold standard annotated corpora. 1