Population Tests in Lexicography

This study discusses the use of a population test as an empirical method in exploring semantic content of near-synonyms for use in electronic dictionaries. Chapter 1 reviews some of the problems of a conventional dictionary, and suggests how an electronic dictionary could meet these challenges. Current lack of semantic information in linguistic literature hampers the development of electronic dictionaries, which has raised an urgent need to study the implicit knowledge of native speakers. Chapter 2 describes the present study, which aims at exploring what types of semantic information can be obtained with population tests. In this study, the test field comprised of twenty-one Finnish verbs all used to describe a complaining speech act. Many of these words are defined as synonyms in mono- and bilingual dictionaries, and many of them are also classified as expressive (onomatopoetic-descriptive) words, which are especially numerous in the Finnish language. The test population (informants) consisted of 154 (16-18 yrs.; 95 women) native speakers of Finnish. Five semantic features (gender and age of the agent, level of anger, volume of voice, and furiousness of the patient) were tested with multiple choice and open-ended tasks. Chapter 3 discusses the results of this study in the context of their potential use in electronic dictionaries. Population test methodology per se will also be discussed. It seems that population tests are able to give remarkable amount of new information to objectively distinguish near-synonymic words from each other. This test type could offer effective tools for exploring the dimensions of semantic contents of words, which would directly serve in construction of electronic, multidimensional dictionaries

Speeding up the inference in Gaussian process models

In this dissertation Gaussian processes are used to define prior distributions over latent functions in hierarchical Bayesian models. Gaussian process is a non-parametric model with which one does not need to fix the functional form of the latent function, but its properties can be defined implicitly. These implicit statements are encoded in the mean and covariance function, which determine, for example, the smoothness and variability of the function. This non-parametric nature of the Gaussian process gives rise to a flexible and diverse class of probabilistic models. There are two main challenges with using Gaussian processes. Their main complication is the computational time which increases rapidly as a function of a number of data points. Other challenge is the analytically intractable inference, which exacerbates the slow computational time. This dissertation considers methods to alleviate these problems. The inference problem is attacked with approximative methods. The Laplace approximation and expectation propagation algorithm are utilized to give Gaussian approximation to the conditional posterior distribution of the latent function given the hyperparameters. The integration over hyperparameters is performed using a Monte Carlo, a grid based, or a central composite design integration. Markov chain Monte Carlo methods over all unknown parameters are used as a golden standard to which the other methods are compared. The rapidly increasing computational time is cured with sparse approximations to Gaussian process and compactly supported covariance functions. These are both analyzed in detail and tested in experiments. Practical details on their implementation with the approximative inference techniques are discussed. The techniques for speeding up the inference are tested in three modeling problems. The problems considered are disease mapping, regression and classification. The disease mapping and regression problems are tackled with standard and robust observation models. The results show that the techniques presented speed up the inference considerably without compromising the accuracy severely

Experiences in Bayesian Inference in Baltic Salmon Management

We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.Comment: Published in at http://dx.doi.org/10.1214/13-STS431 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

New alternative energy pathway for chemical pulp mills: from traditional fibers to methane production

Chemical pulp mills have a need to diversify their end-product portfolio due to the current changing bio-economy. In this study, the methane potential of brown, oxygen delignified and bleached pulp were evaluated in order to assess the potential of converting traditional fibers; as well as microcrystalline cellulose and filtrates; to energy. Results showed that high yields (380 mL CH4/gVS) were achieved with bleached fibers which correlates with the lower presence of lignin. Filtrates from the hydrolysis process on the other hand, had the lowest yields (253 mL CH4/gVS) due to the high amount of acid and lignin compounds that cause inhibition. Overall, substrates had a biodegradability above 50% which demonstrates that they can be subjected to efficient anaerobic digestion. An energy and cost estimation showed that the energy produced can be translated into a significant profit and that methane production can be a promising new alternative option for chemical pulp mills.Postprint (author's final draft