Applying dynamic Bayesian networks in transliteration detection and generation

Peter Nabende promoveert op methoden die programma’s voor automatisch vertalen kunnen verbeteren. Hij onderzocht twee systemen voor het genereren en vergelijken van transcripties: een DBN-model (Dynamische Bayesiaanse Netwerken) waarin Pair Hidden Markovmodellen zijn geïmplementeerd en een DBN-model dat op transductie is gebaseerd. Nabende onderzocht het effect van verschillende DBN-parameters op de kwaliteit van de geproduceerde transcripties. Voor de evaluatie van de DBN-modellen gebruikte hij standaard dataverzamelingen van elf taalparen: Engels-Arabisch, Engels-Bengaals, Engels-Chinees, Engels-Duits, Engels-Frans, Engels-Hindi, Engels-Kannada, Engels-Nederlands, Engels-Russisch, Engels-Tamil en Engels-Thai. Tijdens het onderzoek probeerde hij om verschillende modellen te combineren. Dat bleek een goed resultaat op te leveren

ADOPTING A SERVICE-DOMINANT LOGIC TO PREDICTION OF PREGNANCY COMPLICATIONS: AN EXPLORATORY STUDY OF MATERNAL HEALTHCARE IN UGANDA

The United Nations listed maternal mortality as a major problem especially in developing countries. Predictive models that predict pregnancy complications have been suggested as an intervention to reduce maternal mortality but at the moment, many are not used in clinical practice. This study proposes a service-dominant perspective as an alternative use of predictive models to create value for maternal healthcare. We conducted an exploratory study in south-eastern Uganda in which we held semi-structured interviews with health practitioners to understand how the maternal healthcare system in Uganda works and how pregnancy complications are predicted. Results were analyzed using components from the service innovation framework. We find that overall, ICT has not been fully exploited to improve access to quality care, improve predictions and to improve collaboration among different practitioners in Uganda. Our findings suggest that by adapting a service-dominant perspective, we can enable predictive models and other technologies to assume an active role in maternal healthcare thereby supporting health practitioners with different skills and knowledge to predict pregnancy complications and hence trigger collaborative value creation. We believe that such an intervention will reduce maternal mortality

Comparison of applying Pair HMMs and DBN models in Transliteration Identification

Transliteration is aimed at dealing with unknown words in Cross Language Information Retrieval (CLIR) and Machine Translation (MT). Most of the transliteration tasks depend on a similarity estimation stage where a model is utilized with the aim of identifying a transliteration match for a given source word. In this paper, we evaluate the application of two related frameworks to transliteration identification. Both frameworks model string similarity as the cost incurred through a series of edit operations. One framework implements Pair Hidden Markov Models (Pair HMMs) (Mackay and Kondrak 2005) while the other implements classes of Dynamic Bayesian Network (DBN) models (Filali and Bilmes 2005). For each Pair HMM, we adapt different algorithms for computing transliteration similarity estimates. For the DBN framework, we modify the DBN classes in (Filali and Bilmes 2005) and specify models from the classes to represent factorizations that we hypothesize could affect the value of a transliteration similarity estimate. Separate tests applying models from the two frameworks result in high transliteration identification accuracy on an experimental setup of Russian-English transliteration. A check on the output from models associated with the two frameworks suggests that there can be improved transliteration identification accuracy through a combination of models