Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization

In Automatic Text Summarization, preprocessing is an important phase to reduce the space of textual representation. Classically, stemming and lemmatization have been widely used for normalizing words. However, even using normalization on large texts, the curse of dimensionality can disturb the performance of summarizers. This paper describes a new method for normalization of words to further reduce the space of representation. We propose to reduce each word to its initial letters, as a form of Ultra-stemming. The results show that Ultra-stemming not only preserve the content of summaries produced by this representation, but often the performances of the systems can be dramatically improved. Summaries on trilingual corpora were evaluated automatically with Fresa. Results confirm an increase in the performance, regardless of summarizer system used.Comment: 22 pages, 12 figures, 9 table

Pricing tranched credit products with generalized multifactor models

The market for tranched credit products (CDOs, Itraxx tranches) is one of the fastest growing segments in the credit derivatives industry. However, some assumptions underlying the standard Gaussian onefactor pricing model (homogeneity, single factor, Normality), which is the pricing standard widely used in the industry, are probably too restrictive. In this paper we generalize the standard model by means of a two by two model (two factors and two asset classes). We assume two driving factors (business cycle and industry) with independent tStudent distributions, respectively, and we allow the model to distinguish among portfolio assets classes. In order to illustrate the estimation of the parameters of the model, an empirical application with Moody's data is also included.

Independent AND-parallel implementation of narrowing

We present a parallel graph narrowing machine, which is used to implement a functional logic language on a shared memory multiprocessor. It is an extensión of an abstract machine for a purely functional language. The result is a programmed graph reduction machine which integrates the mechanisms of unification, backtracking, and independent and-parallelism. In the machine, the subexpressions of an expression can run in parallel. In the case of backtracking, the structure of an expression is used to avoid the reevaluation of subexpressions as far as possible. Deterministic computations are detected. Their results are maintained and need not be reevaluated after backtracking