Acumen:
International Journal of Multidisciplinary Research
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Acumen: International Journal of Multidisciplinary Research
The importance of computational linguistics in student’s life
Fayzullayeva Mehribon
Abstract
. In the last decade, information and communication technology has
been the essence of our life. New electronic devices such as smartphones, tablets and
computers, and offer different possibilities of accessing the internet anytime,
anywhere. The computer is one of the innovative technologies that is used as a
medium between the human-being and language teaching and learning which is
called Computational Linguistics. Computational linguistics is a twofold field that
combines principles of linguistics and computer science to develop computational
models and algorithms for understanding, processing, and generating human
language. The primary goal of computational linguistics is to enable computers to
interact with human language meaningfully and efficiently. It also provides tools and
frameworks that can help students better understand language patterns, grammar,
syntax, and semantics. For instance, Natural Language Processing (NLP) tools can
help in learning new languages, offering translation, grammar correction, and text-to-
speech features.
Key words; Natural language processing, Grammar correction, Translation,
Text-to-speech forms, Linguistics, Language patterns, Language principles.
Introduction.
Our modern era is defined by the continuous and quick development of
electronic devices like computers. As we know, language is one of the most natural
and versatile means of communication, this is what makes it complicated (Seddiki,
2018). For learners, computational linguistics is not just about realizing how
computers process language; it provides essential tools for improving communication,
which will enhance critical thinking, and open up career opportunities in modern
fields (Ahmad, 2022).
As digital tools and language-based technologies become an integral part of
everyday life, computational linguistics offers students the knowledge and skills
which are needed to get competitive in this technology-driven world (Blasband,
2018). As Ahmad (2022) stated: Computer-oriented studies have changed into a
hybrid type called computational linguistics. As an interdisciplinary field,
computational linguistics has a history of nearly half a century. The ultimate goal of
computational linguistics is to explain the basic techniques used to create computer
models for the generation and understanding of natural language. In other words,
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International Journal of Multidisciplinary Research
Volume 1, Issue 4
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Acumen: International Journal of Multidisciplinary Research
Whether for language learning, academic research, computational linguistics offers
students a powerful means of bridging the gap between human language and machine
understanding.
Literature review.
Brief history of Computational Linguistics (CL)
Though the concept of computational linguistics is often connected with AI
development, CL predates AI's development, according to the Association for
Computational Linguistics (Nerbonne, 2019). One of the first examples of CL could
be traced from an attempt to translate text from Russian to English. The thought was
that computers could make systematic calculations faster and more accurately than a
person, so it would not take long to process a language (Yamaguchi, 2017). However,
the complexities found in languages were underestimated, taking much more time
and effort to develop a working program. Early researchers in the 1950s were wildly
optimistic. Researchers thought that with a little bit more work in engineering the
rules and a more complete dictionary of words, they could develop a passable system.
They were wrong. Up until the late 1980s, much work in CL involved coming up
with formal analyses of natural language using carefully designed rules (Hutchins,
2012). This led to very precise systems that could give you lots of information about
the small fragment of language it knows about, but which are limited in domain and
scope.
Starting in the late 80s, early 90s, the trend became to learn grammar rules from
data, rather than specify them. In the 1980s, researchers began to explore statistical
models for machine translation, marking a major departure from the rule-based
systems that had dominated early MT. Statistical machine translation uses large
corpora of bilingual text data to learn translation probabilities. This shift greatly
improved the quality and scalability of machine translation systems.
Applications of Computational Linguistics in Natural Language Processing
The fast-growing research area of computational linguistics and natural language
processing (NLP) is fueled by the hands-on creative roles which the underlying
language technologies play in numerous industrial or scientific applications (Bansal,
2020). Computational linguistics plays a fundamental role in the development and
enhancement of Natural Language Processing (NLP), which is the field of AI
concerned with enabling machines to understand, interpret, and generate human
language. It accelerates translation speed, improving accuracy. One of the earliest and
most prominent applications of computational linguistics in NLP is machine
translation (MT)
,
which allows for automatic translation of text or speech from one
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International Journal of Multidisciplinary Research
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Acumen: International Journal of Multidisciplinary Research
language to another (Seddiki,2020). Furthermore, Computational linguistics has
significantly advanced speech recognition systems, which convert spoken language
into text, enabling machines to understand spoken words and respond accordingly.
Sentiment analysis also involves determining the emotional tone behind a div of
text. This application uses NLP to gauge the sentiment or opinion expressed in online
content, reviews, social media, and customer feedback. Finally, Chatbots and
conversational agents use NLP to interact with users in natural, human-like
conversations. These systems are often powered by computational linguistics models
that enable understanding and generating text based on user input (Muslim, 2018).
Deep Learning and Neural Networks
Computational linguistics plays a key role in the development and application of
learning patterns and neural networks
,
particularly in the field of Natural Language
Processing (NLP)
.
The intersection of these fields has led to groundbreaking
advancements in machine learning models that can understand, generate, and process
human language in the same ways that were once thought to be in the field of science
fiction (Silliman, 2014). One of the fundamental challenges in using deep learning to
natural language is how to represent human language in a way that neural networks
can understand (Makatchev, 2014). Computational linguistics provides the theoretical
framework and the techniques that make this possible. Computational linguistics
emphasizes the importance of linguistic structures such as syntax
,
semantics
,
and
morphology—all of which help improve the performance of neural networks in NLP
tasks (Massaro, 2018). Computational linguistics contributes to the enhancement of
deep learning models in areas like speech recognition and text-to-speech (TTS)
systems. Understanding the intricacies of spoken language, phonetics, and prosody is
essential to improving how neural networks process speech data. The ability to
generate human-like language is a key challenge in NLP. Computational linguistics
provides the theoretical knowledge about sentence structure, coherence, and meaning
that helps guide neural networks in generating fluent, contextually appropriate text.
Conclusion.
To conclude, Computational Linguistics (CL) is an important science in 21st
century, which studies human language production, comprehension, and acquisition
through using computers. computational linguistics holds significant importance in a
student's life because it enhances various aspects of education and personal
development. By integrating language with computational methods, students learn
valuable skills that not only improve their understanding of language itself but also
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International Journal of Multidisciplinary Research
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open doors to emerging fields like artificial intelligence, data science, and
machine learning.
References
1.Bolshakov, I. A., & Gelbukh. A. (2004). COMPUTATIONAL linguistics
models, resources, applications. Ciudad university, Mexico.
2.Bansal, Y. (2016). Insight to computational linguistics. Newdelhi, India. 4.
Retrieved from
http://www.warse.org/IJETER/static/pdf/file/ijeter034102016.pdf
;
3.Calzolari, N. (n.d.). COMPUTATIONAL LINGUISTICS. Encyclopedia of Life
SupportSystems(EOLSS).
Retrieved
from
4.Hutchins, W.J. (1995). Machine translation: A brief history. Oxford Pergamon
Press; 8. Lee, K. w. (2000). English teachers' barriers to the use of computer-assisted
language learning.
5.Mario Bkassiny Yang Li, Sudharman K. Jayaweera, “A Survey on Machine-
Learning Techniques in Cognitive Radios,” IEEE Communications Surveys &
Tutorials, Vol. 15, No. 3, Third Quarter 2013.
6.Sushmita Mitra, Yoichi Hayashi, “Bioinformatics with Soft Computing,”IEEE
Trans. On System, Man and Cybernatics—Part C: Application and Reviews, Vol. 36,
No. 5, September 2006.
