Latent semantic analysis for multiple-type interrelated data objects

Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment ...

• Type: Conference
• Year: 2006
• Where: SIGIR
• Authors: Xuanhui Wang, Jian-Tao Sun, Zheng Chen, ChengXiang Zhai
• Keywords: Co-occurrence, Latent Semantic Analysis, Pairwise Co-occurrences, SIGIR 2006