C63 - Computational Techniques; Simulation ModelingReturn

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand-crafted features and rely on feature-inferring neural network systems based on word embeddings. The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far. Various aspects of word vectors preparation were experimentally examined to draw useful conclusions. The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters. The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.

They are available many modeling and decision making systems. Some of them are based on statistical methods like time series analysis. The general problem of these systems is that they cannot correctly react to the changes of modeled systems and their environment. This paper presents an approach based on the fuzzy expert system application, which is able to represent the expert knowledge about the modeled system behavior. This approach combines the statistical methods with expert knowledge and is able to give appropriate information about the system behavior and help with the decision making process. The presented paper describes general principles of this system and its application for waste production modeling as a part of the decision making of the company for waste treatment. This company is able to optimize its resources and warehouse stock management to minimize the production costs.