A syntactic method of extracting terms from special texts for replenishing domain ontologies

Natural Language Processing (NLP) is one of the principal areas of artificial intelligence. It can be argued that the use of ontologies increases the efficiency of natural language processing. However, most ontologies are built manually and require a lot of work. Thus, the problem of automated ontology replenishment is very relevant. One approach is to develop methods for replenishing ontologies using NLP for specific texts of a certain area. We applied the developed method of replenishing the OntoMathPro mathematical ontology, by extracting new terminology from mathematical documents. We developed a method for processing complex syntactic structures (structures with coordination reduction). The method includes certain rule schemata, conditions under which they are to be applied, and conditions determining the sequence of subtrees for which they are to be performed. In our studies, we investigated typical coordination models for mathematical works and performed experiments with a big mathematical collection.