Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.

Crowdsourcing platforms provide attractive solutions for collecting speech synthesis data for visually impaired person. However, quality control problems remain because of low-quality volunteer workers. In this paper, we propose the design of a crowdsourcing system that allows us to devise quality control methods. We introduce four worker selection methods; preprocessing filtering, real-time filtering, post-processing filtering, and guess-processing filtering. These methods include a novel approach that utilizes a collaborative filtering technique in addition to a basic approach involving initial training or use of gold-standard data. These quality control methods improved the quality of collected speech synthesis data. Moreover, we have already collected 140,000 Japanese words from 500 million web data for speech synthesis data.

In this paper, we propose an approach that automatically extract users’ activities in sentences retrieved from Twitter. We then design a timeline action networkbased on Web Ontology Language (OWL). By using the proposed activity extraction approach, we can automatically collect data for the action network. Finally, we propose a novel action-based collaborative filtering, which predicts missing activity data, in order to complement this timeline network. Moreover, with a combination of collaborative filtering and natural language processing (NLP), our method can deal with minority actions such as successful actions. Based on evaluation of tweets which related to the massive Tohoku earthquake,we indicated that our timeline action network can provide useful action patterns in real-time. Not only earthquake disaster, our research can also be applied to other disasters and business models, such as typhoon,travel, marketing, etc.