Collecting Semantic Data from Amazon’s Mechanical Turk for a Lexical Knowledge Resource in a Text to Picture Generating System

WordsEye is a system for converting from English text into three-dimensional graphical scenes that represent that text. At the core of WordsEye is the Scenario-Based Lexical Knowledge Resource (SBLR), a unified knowledge base and representational system for expressing lexical and real-world knowledge needed to depict scenes from text. This paper explores information collection methods for building the SBLR, using Amazon’s Mechanical Turk (AMT) and manual normalization of raw AMT data. The paper follows with a review of existing relations in the SBLR and classification of the AMT data SBLR semantic relations. Since manual annotation is a time-consuming and expensive approach, we also explored the use of automatic normalization of AMT data through WordNet similarity measures and log-odds and log-likelihood ratios extracted from large corpora.