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LEXICAL BORROWINGS AND NEOLOGISMS IN THE MINING
INDUSTRY: THE IMPACT OF AUTOMATION AND ARTIFICIAL
INTELLIGENCE
Kodirova D.Sh.
Senior lecturer
Navoi State University
English Linguistics department
https://doi.org/10.5281/zenodo.15496864
Abstract
This study examines the evolution of mining terminology driven by the
rapid integration of automation and artificial intelligence (AI) technologies.
Through a corpus based and terminological analysis, it investigates lexical
borrowings and neologisms, tracing their etymological origins, semantic shifts,
and pragmatic functions within industrial discourse. The research highlights the
interplay of globalization, technological innovation, and multilingual
communication in reshaping mining lexicons, emphasizing the need for
standardized terminological resources to enhance effective communication and
education in global mining contexts. The findings underscore the sociolinguistic
implications of these changes, particularly in multilingual settings influenced by
Uzbek and Russian linguistic traditions.
Keywords
: mining terminology, lexical borrowing, neologisms,
automation, artificial intelligence, AI-driven mining, multilingual
communication, digital transformation, terminology management, industrial
discourse, calquing, semantic extension, terminological convergence.
Introduction
The mining industry, historically rooted in stable technical lexicons
and occupational jargon, is undergoing a profound linguistic
transformation driven by the adoption of automation and artificial
intelligence (AI). These technologies introduce novel tools, processes, and
operational paradigms, necessitating new terms, semantic adaptations of
existing vocabulary, and borrowings from interdisciplinary fields such as
computer science, robotics, and data science. This paper explores the
emergence of lexical borrowings and neologisms in mining terminology,
analyzing their etymological sources, functional roles, and implications for
industrial communication. Drawing on theoretical frameworks from Uzbek
and Russian linguistics, it examines how these linguistic shifts reflect
technological advancements and facilitate multilingual communication in
global mining operations.
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The rapid globalization of the mining industry, coupled with the
integration of AI-driven systems, has accelerated cross-disciplinary and
cross-linguistic lexical exchange. This phenomenon is particularly evident in
regions with robust mining traditions, such as Russia and Uzbekistan, where
local linguistic systems adapt global terms to fit cultural and technical
contexts. This study aims to provide a comprehensive analysis of these
terminological changes, addressing their ori- gins, adaptation mechanisms,
and sociolinguistic impacts, while advocating for the development of
updated, multilingual terminological resources.
Theoretical Background
Lexical borrowing, the adoption of words from one language into
another, and neologism, the creation of new terms or the reassignment of
meanings to existing ones, are fundamental processes in the evolution of
specialized vocabularies. These mechanisms are complemented by
calquing
, where foreign terms are translated literally into the target
language, preserving structural equivalence (e.g., English “digital twin”
becoming Uzbek “raqamli egizak”).
Semantic extension
, the broadening or
specialization of a term’s meaning, facilitates the adaptation of existing
lexemes to new technological contexts, as seen in the shift of “cloud” from
meteorology to computing in “cloud mining” [3]. Additionally,
terminological
standardization
ensures consistency and precision in technical lexicons, a
critical factor in multilingual industrial settings [7].
Russian linguists, such as Vinogradov (1986), have emphasized the
sociolinguistic functions of borrowed terms, which bridge knowledge gaps
across disciplines and cultures [9]. Superanskaya (1973) further
underscores the importance of
terminological systematization
, where terms
are organized into coherent systems to enhance clarity and interoperability in
technical communication. Uzbek scholars, such as Tursunov (2015) and
Bobojonov (2021), explore
terminological adaptation
, where foreign terms are
phonologically and morphologically adjusted to align with local linguistic
norms, as in the Uzbek term “aqlli datchik” (smart sensor) [8, 1].
Recent linguistic scholarship introduces additional concepts relevant to
mining terminology.
Terminological convergence
, the unification of terms
across languages due to shared technological frameworks, is evident in the
global adoption of terms like “blockchain” [2].
Lexical hybridization
, where
terms blend elements from multiple languages or domains, reflects the
interdisciplinary nature of AI-driven mining (e.g., “autonomation” blending
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“automation” and “au- tonomous”). Furthermore,
pragmatic adaptation
- the
adjustment of terms to suit specific communicative contexts plays a role in
ensuring that borrowed and new terms are functional in operational settings
[5]. These concepts collectively highlight the dynamic interplay between
technological innovation and linguistic evolution, necessitating robust
frameworks for terminology management.
The theoretical framework is further enriched by
onomasiology
, the
study of naming processes, which explains how new concepts in AI-driven
mining are assigned terms, either through borrowing, neologism, or
adaptation. Conversely,
semasiology
, the study of meaning changes,
elucidates how terms like “sensor” evolve from general to specialized
meanings in mining con- texts. These linguistic processes are shaped by the
global dominance of English as a lingua franca in technology, which
influences the direction of borrowing and adaptation in languages like Uzbek
and Russian [3].
Methodology
This study employs a qualitative methodology, integrating corpus-based
analysis with terminological review. A comprehensive corpus of mining
related documents from 2015 - 2024 was compiled, including industry
reports, academic articles, technical glossaries, manuals, and confer- ence
proceedings from international mining forums. Keywords associated with
automation and AI were extracted using text-mining tools and analyzed for
their etymology, semantic fields, and pragmatic contexts. Terminological
databases (e.g., International Council on Mining and Metals Glossary), AI
lexicons (e.g., Global AI Alliance), and scholarly works by Uzbek and Russian
linguists were consulted to trace term origins and adaptation patterns.
The analysis categorized terms into lexical borrowings and
neologisms based on their formation mechanisms. Borrowings were
identified by their foreign language origins, while neologisms were
classified by their novelty or semantic innovation within mining contexts.
A comparative analysis of Uzbek and Russian terminological adaptations was
conducted to assess multilingual influences. The methodology also incorporated
a sociolinguistic lens to evaluate the communicative impact of these terms in
diverse industrial settings.
Results and Discussion
Lexical Borrowings
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Lexical borrowings in mining terminology predominantly originate
from English-dominated fields such as computer science, electronics, and
robotics, reflecting the global influence of technological innovation. These
terms are often adapted through calquing or phonological assimilation in
Russian and Uzbek lexicons. Table 1 presents key examples, their origins,
and their applications in mining contexts.
These borrowings illustrate terminological convergence, as global
technological terms are integrated into local lexicons. For instance, the
Russian term “блокчейн” retains the English phonological structure, while
Uzbek adaptations like “raqamli egizak” employ calquing to preserve
semantic equivalence. This process ensures compatibility with local linguistic
systems while maintaining technical precision.
Table 1. Lexical Borrowings in Mining Terminology
Term
Origin
Description
Smart
Sensor
Electronics
AI-enabled sensors for
self-calibration
and
monitoring
Blockchain
Information
Technology
Decentralized
ledger
for
mining
transactions
and
supply chains
Digital Twin
Engineering
Virtual models for real-time
simulation
of
mining
systems
Cloud
Mining
Cloud Computing
Remote
cryptocurrency
mining
using
shared
processing power
Drone
Surveying
Aviation/Technology Use of UAVs for geological
mapping
Predictive
Maintenance
Data Science
AI
systems
forecasting
equipment failures
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Geofencing
Security Technology Digital
boundaries
for
mining zone security
Machine
learning
Data Science
Algorithms for optimizing
mining processes
Neologisms
Neologisms in mining terminology often arise through blending,
compounding, or semantic extension to describe AI-driven processes. These
terms reflect the need for concise, precise descriptors for novel technologies
and operational paradigms. Table 2 lists key examples, their formation
mechanisms, and their meanings.
Russian and Uzbek scholars, such as Gak (1972) and Mamadaliyev (2019),
highlight the role of lexical hybridization in creating these terms, which
combine elements from multiple domains to address complex processes [4,
6]. For example, “autonomation” blends automation and autonomy,
reflecting a shift toward semi-independent systems.
Sociolinguistic Implications
The adoption of lexical borrowings and neologisms in mining
terminology has significant sociolinguistic implications, particularly in
multilingual industrial contexts. The global dominance of English as a
technical lingua franca creates a power dynamic where non-English-
speaking communities must balance the adoption of foreign terms with the
preservation of local linguistic identities. In Russia, the integration of terms
like “блокчейн” reflects a pragmatic acceptance of English-derived terms,
yet efforts to standardize Russian equivalents (e.g., “облачная добыча” for
cloud mining) demonstrate a commitment to linguistic sovereignty [9].
In Uzbekistan, terminological adaptation is shaped by the need to align
foreign terms with the phonetic and morphological structures of the Uzbek
language. For instance, “aqlli datchik” (smart sensor) maintains the
semantic core of the English term while conforming to Uzbek linguistic
norms [1]. This process of pragmatic adaptation ensures that terms are
accessible to local workers and engineers, fostering effective communication
in operational settings.
Moreover, the proliferation of AI-driven terminology raises questions about
accessibility and inclusivity. Technical terms like “digital twin” or
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“predictive maintenance” may be less familiar to workers without
advanced technological training, potentially creating communication
barriers. This underscores the need for educational initiatives and
multilingual glossaries to bridge knowledge gaps. The sociolinguistic impact
also extends to intercultural communication, as mining companies operate
across diverse linguistic regions, necessitating standardized yet adaptable
terminological systems [5].
Table 2. Neologisms in Mining Terminology
Term
Formation
Description
Example Context
Autonomation
Blend
(Automation
+
Autonomous )
Semi-independent
mining equipment
with
minimal
human oversight
Autonomous
haul trucks
AI-Driven
Ventilation
Compound
Ventilation systems
optimized by AI
algorithm
Underground
mine airflow
Digimine
Compound
Fully digitalized, AI-
integratedmining
operations
Smart
mine
ecosystems
Smart Blasting
Compound
AI-optimized
blasting techniques
Precision
explosives
Auto Drill
Compound
AI-programmed
drilling systems
Automated
exploration rigs
Mine Bot
Blend (Mine +
robot )
Robotic units for
hazardous
environment
exploration
Tunnel Inspection
robots
Eco Mining
Compound
AI-regulated
ventilation software
Reduced
environmental
impact
Vent Smart
Blend
AI-regulated
Real-time
air
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(Ventilation+
smart )
ventilation software quality control
Data Mine
Compound
AI-driven
data
analysis
for
resource extraction
Predictive
ore
modeling
Conclusion
The integration of automation and AI in the mining industry has
catalyzed a profound terminological evolution, characterized by lexical
borrowings from technology related fields and the creation of neologisms
through blending, compounding, and semantic extension. These linguistic
innovations enhance precision in describing novel tools, processes, and
operational paradigms, reflecting the industry’s digital transformation. The
study highlights the importance of terminological convergence,
standardization, and adaptation in facilitating effective communication in
multilingual mining contexts.
The sociolinguistic implications of these changes underscore the need for
robust terminology management systems, including AI-powered databases
and multilingual glossaries, to support global mining operations. Future
research should explore the development of such resources, the impact of
terminological convergence on intercultural communication, and the role of
onomasiological and semasiological processes in shaping technical lexicons.
By addressing these challenges, the mining industry can ensure that its
linguistic evolution keeps pace with its technological advancements
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4.
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