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.

Understanding Online Game Players' Post-adoption Behavior: an Investigation of Social Network Games in China

Retaining players and re-attracting switching players has long been a central topic for SNG providers with regard to the post-adoption stage of playing an online game. However, there has not been much research which has explored players’ post-adoption behavior by incorporating the continuance intention and the switching intention. In addition, traditional IS continuance theories were mainly developed to investigate users’ continued use of utilitarian IS, and thus they may fall short when trying to explain the continued use of hedonic IS. Furthermore, compared to the richer literature on IS continuance, far too little attention has been paid to IS switching, leading to a dearth of knowledge on the subject, despite the increased incidence of the switching phenomenon in the IS field. By addressing the limitations of prior literature, this study seeks to examine the determinants of SNG players’ two different post-adoption behaviors, including the continuance intention and the switching intention. This study takes a positivist approach and uses survey research method to test five proposed research models based on Unified Theory of Acceptance and Use of Technology 2; Use and Gratification Theory; Push-Pull-Mooring model; Cognitive Dissonance Theory; and a self-developed model respectively with empirical data collected from the SNG players of one of the biggest SNG providers in China. A total of 3919 valid responses and 541 valid responses were used to examine the continuance intention and the switching intention, respectively. SEM is utilized as the data analysis method. The proposed research models are supported by the empirical data. The continuance intention is determined by enjoyment, fantasy, escapism, social interaction, social presence, social influence, achievement and habit. The switching intention is determined by enjoyment, satisfaction, subjective norms, descriptive norms, alternative attractiveness, the need for variety, change experience, and adaptation cost. This study contributes to IS theories in three important ways. Firstly, it shows IS switching should be included in IS post-adoption research together with IS continuance. Secondly, a modern IS is usually multi-functional and SNG players have multiple reasons for using a SNG, thus a player’s beliefs about the hedonic, social and utilitarian perceptions of their continued use of the SNG exert significant effects on the continuance intention. Thirdly, the determinants of the switch ing intention mainly exert push, pull, and mooring effects. Players’ beliefs about their current SNG and the available alternatives, as well as their individual characteristics are all significant determinants of the switching intention. SNG players combine these effects in order to formulate the switching intention. Finally, this study presents limitations and suggestions for future research.

A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals

In the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (HRV). First, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. In order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. It is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. The merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. Our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. Furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of HRV signals