Effect of Tuned Parameters on a LSA Multiple Choice Questions Answering ModelReport as inadecuate




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1 MALIRE - Machine Learning and Information Retrieval LIP6 - Laboratoire d-Informatique de Paris 6 2 LPC - Laboratoire de psychologie cognitive

Abstract : 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.

Keywords : LSA software Unsupervised Learning Vector Space Model Dimensionality MCQ Answering Model Semantic similarity Latent Semantic Analysis LSA





Author: Alain Lifchitz - Sandra Jhean-Larose - Guy Denhière -

Source: https://hal.archives-ouvertes.fr/



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