Abstract

The last time I spoke at the reading group, Ted raised the point that Random Indexing might do as well as other word space models without any of the comlicated machinery. Let’s decide for ourselves…and besides, you will probably find that random indexing is useful to you!

I will be covering the paper: @conference{sahlgren2005introduction, title={{An introduction to random indexing}}, author={Sahlgren, M.}, booktitle={Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering, TKE 2005 }, year={2005}, organization={Citeseer} }

Word space models enjoy considerable attention in current research on se- mantic indexing. Most notably, Latent Semantic Analysis/Indexing (LSA/LSI; Deerwester et al., 1990, Landauer & Dumais, 1997) has become a household name in information access research, and deservedly so; LSA has proven its mettle in numerous applications, and has more or less spawned an entire research field since its introduction around 1990. Today, there is a rich flora of word space models available, and there are numerous publications that report exceptional results in many different applications, including in- formation retrieval (Dumais et al., 1988), word sense disambiguation (Schütze, 1993), various semantic knowledge tests (Lund et al., 1995, Karlgren & Sahlgren, 2001), and text categorization (Sahlgren & Karlgren, 2004).

This paper introduces the Random Indexing word space approach, which presents an efficient, scalable and incremental alternative to standard word space methods. The paper is organized as follows: in the next section, we re- view the basic word space methodology. We then look at some of the prob- lems that are inherent in the basic methodology, and also review some of the solutions that have been proposed in the literature. In the final section, we in- troduce the Random Indexing word space approach, and briefly review some of the experimental results that have been achieved with Random Indexing.