This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template filling of disorder attributes. The task was comprised of two subtasks: attribute normalization (task 2a) and cue identification (task 2b). We instructed participants to develop a system which either kept or updated a default attr...

Generative unigram language models have proven to be a simple though effective model for information retrieval tasks. In contrast to ad-hoc retrieval, topic tracking requires that matching scores are comparable across topics. Several ranking functions based on generative language models: straight likelihood, likelihood ratio, normalized likelihood ratio, and the related Kullback-Leibler diverge...

a parallel hybrid system of hmm and gmm modeling techniques was implemented and used in a telephony speaker verification and identification system. spectral subtraction and weighted projection measure were used to render this system more robust against additional noise. cepstral mean subtraction method was also applied for the compensation of convolution noise due to transmission channel degrad...

This report outlines the Task 1 of the ShARe/CLEF eHealth evaluation lab pilot. This task focused on identification (1a) and normalization (1b) of diseases and disorders in clinical reports. It used annotations from the ShARe corpus. A total of 22 teams competed in Task 1a and 17 of them also participated Task 1b. The best systems had an F1 score of 0.75 (0.80 Precision, 0.71 Recall) in Task 1a...

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that could be exploited by strategies such as score normalization. Indeed, it has b...

Previous photos and videos This work explores normalization for parser adaptation. Traditionally, normalization is used as separate preprocessing step. We show that integrating the normalization model into the parsing algorithm is beneficial. To this end, we use a normalization model combined with the parsing as intersection algorithm. This way, multiple normalization candidates can be leverage...

This paper deals with several cohort methods for score normalization in speaker verification systems. At first, the reasons for score normalization are provided. Next, the principle of score normalization techniques based on Bayesian theorem are explained. The world, cohort, and unconstraint cohort normalization techniques are presented. A new normalization technique, unconstraint cohort extrap...

NIST Speaker Recognition Evaluation 2016 has revealed the importance of score normalization for mismatched data conditions. This paper analyzes several score normalization techniques for test conditions with multiple languages. The best performing one for a PLDA classifier is an adaptive s-norm with 30% relative improvement over the system without any score normalization. The analysis shows tha...