[ Team LiB ] Recipe 1.11 Writing Database-Independent Code Problem You need to develop an application that can be used with different data providers, but does not lose functionality that is specific to the different providers.
These criteria set a general framework for monitoring existing and proposed legislation. But monitoring
the actual policy needs of SME is difficult even in advanced economies. There is a need to establish
mechanisms for effective public-private sector dialogue and feedback from those who implement legislation.
Business associations from time to time can undertake studies of members’ needs and governments
anticipating a particularly acute problem or proposing new forms of legislation may commission surveys.
This paper presents a language-independent probabilistic answer ranking framework for question answering. The framework estimates the probability of an individual answer candidate given the degree of answer relevance and the amount of supporting evidence provided in the set of answer candidates for the question. Our approach was evaluated by comparing the candidate answer sets generated by Chinese and Japanese answer extractors with the re-ranked answer sets produced by the answer ranking framework. ...
In this paper we start to explore two-part collocation extraction association measures that do not estimate expected probabilities on the basis of the independence assumption. We propose two new measures based upon the well-known measures of mutual information and pointwise mutual information.
We present a simple, language-independent method for integrating recovery of empty elements into syntactic parsing. This method outperforms the best published method we are aware of on English and a recently published method on Chinese.
This paper describes a new discourse module within our multilingual NLP system. Because of its unique data-driven architecture, the discourse module is language-independent. Moreover, the use of hierarchically organized multiple knowledge sources makes the module robust and trainable using discourse-tagged corpora. Separating discourse phenomena from knowledge sources makes the discourse module easily extensible to additional phenomena.
We present a language-pair independent terminology extraction module that is based on a sub-sentential alignment system that links linguistically motivated phrases in parallel texts. Statistical ﬁlters are applied on the bilingual list of candidate terms that is extracted from the alignment output. We compare the performance of both the alignment and terminology extraction module for three different language pairs (French-English, French-Italian and French-Dutch) and highlight languagepair speciﬁc problems (e.g. different compounding strategy in French and Dutch). ...
Many current approaches to statistical language modeling rely on independence a.~sumptions 1)etween the different explanatory variables. This results in models which are computationally simple, but which only model the main effects of the explanatory variables oil the response variable. This paper presents an argmnent in favor of a statistical approach that also models the interactions between the explanatory variables. The argument rests on empirical evidence from two series of experiments concerning automatic ambiguity resolution. ...
In this chapter, the basic concepts of independent component analysis (ICA) are deﬁned. We start by discussing a couple of practical applications. These serve as motivation for the mathematical formulation of ICA, which is given in the form of a statistical estimation problem. Then we consider under what conditions this model can be estimated, and what exactly can be estimated.
In the preceding chapters, we introduced several different estimation principles and
algorithms for independent component analysis (ICA). In this chapter, we provide
an overview of these methods. First, we show that all these estimation principles
are intimately connected, and the main choices are between cumulant-based vs.
negentropy/likelihood-based estimation methods, and between one-unit vs. multiunit
methods. In other words, one must choose the nonlinearity and the decorrelation
This chapter deals with applications of independent component analysis (ICA) and blind source separation (BSS) methods to telecommunications. In the following, we concentrate on code division multiple access (CDMA) techniques, because this speciﬁc branch of telecommunications provides several possibilities for applying ICA and BSS in a meaningful way.
Chapter 3 REFINEMENTS OF THE LIMIT THEOREMS FOR NORMAL CONVERGENCE
§ 1 . Introduction In this chapter we consider a sequence X 1 , X2 , . . . of independent, identically distributed random variables belonging to the domain of attraction of the normal law. As shown in § 2 .6, the X; necessarily have a finite variance a 2 .
Chapter 7 RICHTER'S LOCAL THEOREMS AND BERNSTEIN'S INEQUALITY
1 . Statement of the theorems The theorems of this chapter do not have a collective character, and are related to Theorem 6 .1.1 . We shall consider a sequence of independent, identically distributed random variables XX
In this chapter, we review central concepts of probability theory,statistics, and random processes. The emphasis is on multivariate statistics and random vectors. Matters that will be needed later in this book are discussed in more detail, including, for example, statistical independence and higher-order statistics.
A difﬁcult problem in independent component analysis (ICA) is encountered if the number of mixtures xi is smaller than the number of independent components si. This means that the mixing system is not invertible: We cannot obtain the independent components (ICs) by simply inverting the mixing matrix . Therefore, even if we knew the mixing matrix exactly, we could not recover the exact values of the independent components.
Independent Component Analysis. Aapo Hyv¨ rinen, Juha Karhunen, Erkki Oja a Copyright 2001 John Wiley & Sons, Inc. ISBNs: 0-471-40540-X (Hardback); 0-471-22131-7 begin_of_the_skype_highlighting 0-471-22131-7 end_of_the_skype_highlighting (Electronic)
Methods using Time Structure
The model of independent component analysis (ICA) that we have considered so far consists of mixing independent random variables, usually linearly. In many applications, however, what is mixed is not random variables but time signals, or time series.
In this chapter, we present some additional extensions of the basic independent component analysis (ICA) model. First, we discuss the use of prior information on the mixing matrix, especially on its sparseness. Second, we present models that somewhat relax the assumption of the independence of the components.