Human-computer interactions require systems that work out of the box without requiring lots of data to adapt to a new task or user. In this research, we address low resource spoken language understanding tasks such as...
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Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve ...
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Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.
The PI+CI is a reset compensator that has been shown to be effective in a number of practical applications. In this work, a simple PI+CI tuning method for integrating systems with time delay is proposed. It is a direc...
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We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment i...
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ISBN:
(纸本)9781586034528
We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment is used to derive an initial set of collocations, these are used in turn to improve the alignment and this new alignment is used to generate new collocations. This process is repeated until no more collocations are found. The final alignment and the set of collocations are used to train a translation model. We use a model that is based on finite state transducers and word clusters and has been modified to work with collocations in addition to single words. We present experiments in which we show that automatic collocations improve translation quality without prior linguistic information.
This paper presents a mathematical technique for modeling the generation of Grid-solutions employing a Case based reasoning system (CBR). Roughly speaking, an intelligent system that tries to be adapted to highly dyna...
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Fuzzy ontologies extend classical ontologies to allow the representation of imprecise and vague knowledge. Although a relatively important amount of work has been carried out in the last years and they have been succe...
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The aim of this work is to present an experiment of liquid level process controlled by a reset PI+CI compensator. The results are compared with a well tuned linear PI compensator, showing that the reset compensator im...
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The UAV-mounted base station has been integrated into 5G and beyond to provide communication in an emergency when conventional systems fail due to some reason. Moreover, it is also used in coverage enhancement and for...
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Classical ontologies are not suitable to represent imprecise or vague information, which has led to several extensions using non-classical logics. In particular, several fuzzy extensions have been proposed in the lite...
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The interaction between electromagnetic radiation and infinite plane shield having finite thickness d, placed in free space in the plane waves region depends on material/shield macroscopic parameters (e, fl, a), its t...
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