LiMoSiNe pipeline: Multilingual UIMA-based NLP platform

We present a robust and efficient parallelizable multilingual UIMA-based platform for automatically annotating textual inputs with different layers of linguistic description, ranging from surface level phenomena all the way down to deep discourse-level information. In particular, given an input text, the pipeline extracts: sentences and tokens; entity mentions; syntactic information; opinionated expressions; relations between entity mentions; co-reference chains and wikified entities. The system is available in two versions: a standalone distribution enables design and optimization of userspecific sub-modules, whereas a server-client distribution allows for straightforward highperformance NLP processing, reducing the engineering cost for higher-level tasks

Auditory-inspired morphological processing of speech spectrograms: applications in automatic speech recognition and speech enhancement

New auditory-inspired speech processing methods are presented in this paper, combining spectral subtraction and two-dimensional non-linear filtering techniques originally conceived for image processing purposes. In particular, mathematical morphology operations, like erosion and dilation, are applied to noisy speech spectrograms using specifically designed structuring elements inspired in the masking properties of the human auditory system. This is effectively complemented with a pre-processing stage including the conventional spectral subtraction procedure and auditory filterbanks. These methods were tested in both speech enhancement and automatic speech recognition tasks. For the first, time-frequency anisotropic structuring elements over grey-scale spectrograms were found to provide a better perceptual quality than isotropic ones, revealing themselves as more appropriate—under a number of perceptual quality estimation measures and several signal-to-noise ratios on the Aurora database—for retaining the structure of speech while removing background noise. For the second, the combination of Spectral Subtraction and auditory-inspired Morphological Filtering was found to improve recognition rates in a noise-contaminated version of the Isolet database.This work has been partially supported by the Spanish Ministry of Science and Innovation CICYT Project No. TEC2008-06382/TEC.Publicad

The Rényi Entropies Operate in Positive Semifields

We set out to demonstrate that the Rényi entropies are better thought of as operating in a type of non-linear semiring called a positive semifield. We show how the Rényi’s postulates lead to Pap’s g-calculus where the functions carrying out the domain transformation are Rényi’s information function and its inverse. In its turn, Pap’s g-calculus under Rényi’s information function transforms the set of positive reals into a family of semirings where “standard” product has been transformed into sum and “standard” sum into a power-emphasized sum. Consequently, the transformed product has an inverse whence the structure is actually that of a positive semifield. Instances of this construction lead to idempotent analysis and tropical algebra as well as to less exotic structures. We conjecture that this is one of the reasons why tropical algebra procedures, like the Viterbi algorithm of dynamic programming, morphological processing, or neural networks are so successful in computational intelligence applications. But also, why there seem to exist so many computational intelligence procedures to deal with “information” at large

Systems vs. Methods: an Analysis of the Affordances of Formal Concept Analysis for Information Retrieval ⋆

Abstract. We review previous work using Formal Concept Analysis (FCA) to build Information Retrieval (IR) applications seeking a wider adoption of the FCA paradigm in IR. We conclude that although a number of systems have been built with such paradigm (FCA in IR), the most effective contribution would be to help establish IR on firmer grounds (FCA for IR). Since such an approach is only incipient, we contribute to the general discussion by discussing affordances and challenges of FCA for IR.

(Color online) Entropy triangle for the MEG mind Reading data ordered after accuracy (A) and a detail of the participants of higher accuracy (B).

<p>The ranking following accuracy is at odds with the EMA and the NIT factor ranking based in mutual information (height, right scale of triangle). The detail in (B) shows that participant , closely followed by should have been ranked first after this criterion. </p

(Color online) Entropy decomposition for square matrices of (A) , (B) , and (C) (decimated), representing confusion matrices for a classification task at different accuracy levels as described by the right color bar.

<p>The interspersing of the plots representing matrices with different accuracies but similar entropies is evident at all levels for and but only for lower levels of accuracy for . This entails that accuracy is not a good criterion to judge the flow of information from the input labels to the output labels of a classifier (see text).</p

Schematic Entropy Triangle showing interpretable zones and extreme cases of classifiers.

<p>The annotations on the center of each side are meant to hold for that whole side.</p