Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

A Phase Model for Building a Customer Value Based Sales Tool – Case Mining and Metals Industry

The objective of this research is to observe the state of customer value management in Outotec Oyj, determine the key development areas and develop a phase model with which to guide the development of a customer value based sales tool. The study was conducted with a constructive research approach with the focus of identifying a problem and developing a solution for the problem. As a basis for the study, the current literature involving customer value assessment and solution and customer value selling was studied. The data was collected by conducting 16 interviews in two rounds within the company and it was analyzed by coding openly. First, seven important development areas were identified, out of which the most critical were “Customer value mindset inside the company” and “Coordination of customer value management activities”. Utilizing these seven areas three functionality requirements, “Preparation”, “Outotec’s value creation and communication” and “Documentation” and three development requirements for a customer value sales tool were identified. The study concluded with the formulation of a phase model for building a customer value based sales tool. The model included five steps that were defined as 1) Enable customer value utilization, 2) Connect with the customer, 3) Create customer value, 4) Define tool to facilitate value selling and 5) Develop sales tool. Further practical activities were also recommended as a guide for executing the phase model.

The demand-supply balancing process in supply chain management - case mining & metals industry equipment

The aim of this thesis is to search how to match the demand and supply effectively in industrial and project-oriented business environment. The demand-supply balancing process is searched through three different phases: the demand planning and forecasting, synchronization of demand and supply and measurement of the results. The thesis contains a single case study that has been implemented in a company called Outotec. In the case study the demand is planned and forecasted with qualitative (judgmental) forecasting method. The quantitative forecasting methods are searched further to support the demand forecast and long term planning. The sales and operations planning process is used in the synchronization of the demand and supply. The demand forecast is applied in the management of a supply chain of critical unit of elemental analyzer. Different meters on operational and strategic level are proposed for the measurement of performance.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Determinants of capital expenditures: Evidence from oil and mining sectors in Russia

This master’s thesis investigates the significant macroeconomic and firm level determinants of CAPEX in Russian oil and mining sectors. It also studies the Russian oil and mining sectors, its development, characteristics and current situation. The panel data methodology was implemented to identify the determinants of CAPEX in Russian oil and mining sectors and to test derived hypotheses. The core sample consists of annual financial data of 45 publicly listed Russian oil and mining sector companies. The timeframe of the thesis research is a six year period from 2007 to 2013. The findings of the master’s thesis have shown that Gross Sales, Return On Assets, Free Cash Flow and Long Term Debt are firm level performance variables along with Russian GDP, Export, Urals and the Reserve Fund are macroeconomic variables that determine the magnitude of new capital expenditures reported by publicly listed Russian oil and mining sector companies. These results are not controversial to the previous research paper, indeed they confirm them. Furthermore, the findings from the emerging countries, such as Malaysia, India and Portugal, are analogous to Russia. The empirical research is edifying and novel. Findings from this master’s thesis are highly valuable for the scientific community, especially, for researchers who investigate the determinant of CAPEX in developing countries. Moreover, the results can be utilized as a cogent argument, when companies and investors are doing strategic decisions, considering the Russian oil and mining sectors.