In the course of the last two decades, significant progress has been

made with regard to the automatic extraction of meaning from

large-scale text corpora. The most successful models are based on

distributional data, calculating the meaning of words according to the

contexts in which those words appear. In this presentation, we'll look

at a number of factorization models that are able to induce latent

semantics from distributional data. First, we'll look at matrix

factorization. Matrix factorization models are useful for the

induction of topical dimensions, which is useful for a number of

applications. Next, we'll look at a number of tensor factorization

models. Where matrices are restricted to two-way data, tensors are

able to handle multi-way co-occurrences. Using tensors - combined with

appropriate factorization models - we are able to build semantically

richer language models, that are useful in applications such as

selectional preference induction and the modeling of semantic

compositionality.