# A Data Driven Approach for Raw Material Terminology
###### Abstract
One such dictionary called MobiLex was produced at the Stellenbosch University in South Africa to enhance teaching and learning of historical terms, with favorable pedagogical consequences regarding the learning of such terms. Trends and developments in technology offer the possibility of changing the face of lexicographical support in a mobile environment, from a pedagogical perspective [6]. Big data analysis methods have opened new possibilities for analyzing corpora, which contain large amounts of textual data. Thus, for example, [PERSON] et al. [7] propose a novel statistic-based corpus machine processing approach to refine big textual data, to be used for ESP (English for Specific Purposes). The approach is based on establishing a function word list and embedding it into the program, in order to refine the word list and keyword list. The aim is to enhance the efficiency of corpora processing, starting from preparatory work, followed by generating raw data, optimizing the process, and ending by generating refined data. COVID-19 news reports are used as a simulation example of big textual data and applied to verify the efficacy of the machine optimizing process. Electronic lexicography offers important possibilities in comparison to the traditional approach. Examples of usage may be extracted from original texts and linked to dictionary entries. There are practically no limitations to the amount of data that can be added, including multimedial data, which results in better quality data. Various search options and different possibilities of database organization contribute to the efficiency of access. Dictionaries can be easily customized for specific needs of users' groups. Electronic lexicography also enables hybridization, by breaking limits between different types of language resources--for example, dictionaries, encyclopedias, term banks, lexical databases, translation tools and the like. Finally, active user involvement is possible, by enabling collaborative or community-based input to dictionaries [8]. This paper presents a data driven approach aimed at using opportunities offered by electronic lexicography, as well as various available techniques of Natural Language Processing (NLP), to develop a semi-automatic pipeline for dictionary production. The approach is focused on raw material terminology, with an emphasis on terminology related to the mining industry, as a case study, the main goal being to cover Serbian and bilingual English-Serbian terminology in the raw material domain, within a system that can be used for developing web and mobile dictionary applications. In developing this system, a data driven approach is adopted, relying on available textual, lexical and terminological resources, both in printed and electronic form. Within the development of this system, printed resources, the paper dictionaries covering raw material terminology, were subjected to systematic extensive digitisation. In this approach, besides compiling a comprehensive multilingual lexical database of raw material terminology, lexicographic methods for automatic knowledge extraction are used, including corpus data analysis, automatic data extraction, editing and publishing extracted data in (online) dictionaries. Using extracted lexicographically relevant data (lemma lists, example sentences, collocations) as complementary resources in electronic dictionaries is known as the one-click dictionary or push-pull dictionary model, which is used, for example, in the Sketch-engine [9] for several languages, but has not yet been used for Serbian. A similar approach to the one outlined in this paper was applied in development of the Sonaveeb language portal of the Institute of the Estonian Language, which contains data from a number of dictionaries and termbases, with a total of 200,000 Estonian headwords with collocations, etymology, multi-word expressions, etc.