Analysis of Images, Social Networks and Texts. 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers. Communications in Computer and Information Science
Organizational citizenship behavior (OCB) is an important management construct. Despite previous investigations in relation to social capital, the role of networks in its emergence has received only limited attention. In this paper we investigate the relationship between OCB, with data collected from supervisors evaluating their subordinates; several types of organizational networks (professional, friendship, support, supervisor-subordinate), and several other constructs (collected from the employees themselves), shown to affect OCB in the past. All data were collected at a large insurance company in Russia. Outcomes of this study have several important implications. First, the impact of networks on manifestation of OCB depends not only on the strength of network ties, but on types of network. Second, interorganizational relationships are complex and consist of several levels of mediated relationships. Results of this study can impact the theoretical understanding of OCB and have practical implications for the supervisor-subordinate relationships in the workplace.
Modern corpora provide suitable access to the stored data. However, they are convenient rather for researchers than for students learning a foreign language and not familiar with the corpus linguistics. Therefore, we set the task of creating a corpus, which contains information on words co-occurrence, their syntactical relations and their government for the Russian language.
In this paper we suggest the first systematic review and com- pare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in DotA 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that pur- pose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best re- sults from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.
In this paper we present a comparison of three morphological taggers for Russian with regard to the quality of morphological disambiguation performed by these taggers. We test the quality of the analysis in three different ways: lemmatization, POS-tagging and assigning full morphological tags. We analyze the mistakes made by the taggers, outline their strengths and weaknesses, and present a possible way to improve the quality of morphological analysis for Russian.