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parsing, semantic interpretation, and pragmatic analysis, NLP systems are capable
of analyzing both the structure and meaning of texts. This capability allows machines
to perform complex linguistic tasks, such as responding to questions, maintaining
conversations, and delivering contextually appropriate information.
Machine Learning
Machine Learning is a key area within artificial intelligence that concentrates on
designing systems capable of learning from data, recognizing patterns, and making
decisions with minimal human input. According to Mitchell, it is the study of
algorithms that improve automatically through experience. This field relies on
statistical models and algorithms that enable systems to enhance their performance
on specific tasks over time. Machine learning is generally divided into three
categories: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning involves training models using labeled datasets, where inputs
correspond to known outputs. Unsupervised learning focuses on identifying hidden
structures within unlabeled data. Reinforcement learning, on the other hand, trains
models to make sequential decisions by rewarding desirable actions and penalizing
undesirable ones. Machine learning has a wide range of applications, including
predictive analytics, recommendation systems, autonomous driving, and fraud
detection.
Neural Networks
Neural Networks are computational models inspired by the structure and
functioning of the human brain, designed primarily for pattern recognition. As
Haykin describes, they are highly parallel and distributed systems composed of
simple processing units, or artificial neurons, capable of acquiring and storing
knowledge through experience. These networks interpret data through processes
such as classification and clustering of raw inputs. Structurally, neural networks
consist of multiple layers, including an input layer, one or more hidden layers, and an
output layer. Each neuron performs a basic computation, and data flows through the
network, being progressively transformed into more meaningful representations.
Common types of neural networks include feedforward networks, convolutional
neural networks (CNNs), and recurrent neural networks (RNNs). Feedforward
networks transmit information in a single direction, CNNs are particularly effective in
image processing, and RNNs are well-suited for sequential data analysis. Neural
networks form the foundation of deep learning, a specialized area of machine
learning, and play a crucial role in advancements in fields such as computer vision,
speech recognition, and NLP.
By integrating NLP, machine learning, and neural networks into literary
research, scholars are able to reveal new patterns and insights within texts, thereby
deepening the understanding of literature through advanced computational
techniques. These technologies support the analysis of extensive literary corpora,
contributing to a more comprehensive interpretation of themes, stylistic features,
and authorial intent, while also fostering new possibilities for interdisciplinary
collaboration.
Traditional approaches to literary analysis primarily depend on close reading
and human interpretation. Although these methods remain valuable, they are often
time-intensive and constrained by individual cognitive limits. In contrast, artificial
intelligence offers the capacity to rapidly process extensive textual corpora, enabling 440
scholars to identify patterns and insights that may not be immediately visible.

III SHO‘BA:

Jahon adabiyoti tadqiqotlarida sun’iy intellekt yordamida badiiy matnlarni
tahlil qilish va interpretatsiya masalalari
https://www.asr-conference.com/