Deep Learning from Web-Scale Corpora for Better Dictionary Interfaces

This paper explores advanced learning mechanisms - neural networks trained by the Word2Vec method - for predicting word associations. We discuss how the approach can be built into dictionary interfaces to help tip-of-the-tongue searches. We also describe our contribution to the CogALex 2014 shared task. We argue that the reverse response-stimulus word associations chosen for the shared task are only mildly related to the motivation idea of the lexical access support system. The methods employed in our contribution are briefly introduced. We present results of experiments with various parameter settings and show what improvement can be expected if more than one answer is allowed. The paper concludes with a proposal for a new collective effort to assemble real tip-of-the-tongue situation records for future, more-realistic evaluations.