Creating semantic representations

Abstract

In this chapter, we present the vector space model and some ways to further process such a representation: With feature hashing, random indexing, latent semantic analysis, non-negative matrix factorization, explicit semantic analysis and word embedding, a word or a text may be associated with a distributed semantic representation. Deep learning, explicit semantic networks and auxiliary non-linguistic information provide further means for creating distributed representations from linguistic data. We point to a few of the methods and datasets used to evaluate the many different algorithms that create a semantic representation, and we also point to some of the problems associated with distributed representations.