Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page

Towards the Quality Improvement of Web Applications by Neuroscience Techniques

User-centered design not only requires designers to analyse and anticipate how users are likely to use a Web application, but also to validate their assumptions with regard to user behaviour in real environments. Cognitive neuroscience, for its part, addresses the questions of how psychological functions are produced by neural circuitry. The emergence of powerful new measurement techniques allows neuroscientists and psychologists to address abstract questions such as how human cognition and emotion are mapped to specific neural substrates. This paper focus on the validation of user-centered designs and requirements of Web applications by neuroscience techniques and suggest the use of these techniques to achieve efficient and effectiveness validated designs by real behavior of potential users.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-RJunta de Andalucía TIC-578