Exploring Mental Lexicon in an Efficient and Economic Way: Crowdsourcing Method for Linguistic Experiments

摘要

Mental lexicon plays a central role in human language competence and inspires the creation of new lexical resources. The traditional linguistic experiment method which is used to explore mental lexicon has some disadvantages. Crowdsourcing has become a promising method to conduct linguistic experiments which enables us to explore mental lexicon in an efficient and economic way. We focus on the feasibility and quality control issues of conducting Chinese linguistic experiments to collect Chinese word segmentation and semantic transparency data on the international crowdsourcing platforms Amazon Mechanical Turk and Crowdflower. Through this work, a framework for crowdsourcing linguistic experiments is proposed.