Parameters driving effectiveness of LSA on topic segmentation

Abstract

Latent Semantic Analysis (LSA) is an efficient statistical technique for extracting semantic knowledge from large corpora. One of the major problems of this technique is the identification of the most efficient parameters of LSA and the best combination between them. Therefore, in this paper, we propose a new topic segmenter to study in depth the different parameters of LSA for the topic segmentation. Thus, the aim of this study is to analyze the effect of these different parameters on the quality of topic segmentation and to identify the most efficient parameters. Based on extensive experiments, we showed that the choice of LSA parameters is very sensitive and it has an impact on the quality of topic segmentation. More important, according to this study, we are able to propose appropriate recommendation for the selection of parameters in the field of topic segmentation.