Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions Automated extraction of semantic information from textual descriptions can be implemented by processing results of application of Stanford CoreNLP tools. This paper presents a sequence of processing steps and initial results of their application for two examples of a description of systemxe2x80x99s functionality. The processing steps allow identifying main functional characteristics of the system and its operational domain. Results obtained as a result of application of the steps are compared with data obtained as a result of analysis by a developer. Application of Stanford CoreNLP parsers in certain cases can produce errors and can influence results of further processing. The comparison of the two results sets showed that variability of language constructs in descriptions affects an amount of implicitly expressed knowledge. Nevertheless, results of this research can be used as a start point of automated text processing for creation of analysis models.

Viability of Neural Networks for Core Technologies for ResourceScarce Languages In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resourcescarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resourcescarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.