| Abstract: | This article 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 consisted of answering
(French) biology multiple choice questions, was used to test the semantic properties of the truncated singular space and to
study the relative influence of the main parameters. A dedicated software was designed to fine-tune the LSA semantic space
for the multiple choice questions task. With optimal parameters, the performances of our simple model were quite surprisingly
equal or superior to those of seventh- and eighthgrade students. This indicates that semantic spaces were quite good despite
their low dimensions and the small sizes of the training data sets. In addition, we present an original entropy global weighting
of the answers’ terms for each of the multiple choice questions, which was necessary to achieve the model’s success. |
