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THE IMPORTANCE OF USING DEEP LEARNING TECHNOLOGIES IN TEXT MINING

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ARTICLE ANNOTATION

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Deep learning technologies have significantly advanced the field of text mining by enhancing the capability to process, analyze, and extract meaningful information from vast amounts of unstructured text data. Key technologies include Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for capturing sequential dependencies in text, Convolutional Neural Networks (CNNs) for text classification, and attention mechanisms and Transformers like BERT and GPT for parallel processing and understanding context. Word embeddings (e.g., Word2Vec, GloVe) provide semantic representations of words, while sequence-to-sequence models enable applications such as text summarization and machine translation. Additionally, self-supervised and zero-shot learning broaden the adaptability of models across various text mining tasks. These technologies drive applications like sentiment analysis, entity recognition, document summarization.

AUTHORS

L.Safarov

Qarshi davlat universiteti

Tags

# машинный перевод# transformers# Sentiment analysis# Deep learning# text mining# RNN# LSTM# CNN# BERT# GPT# diqqat mexanizmlari# so‘zlarni joylashtirish# Word2Vec# GloVe# matn tasnifi# his-tuyg‘ularni tahlil qilish# obyektlarni tanib olish# hujjatlarni umumlashtirish# mavzuni aniqlash# mashina tarjimasi# o‘z-o‘zidan nazorat ostida o‘# nolga o‘rganish.# трансформаторы# механизмы внимания# встраивание слов# классификация текста# анализ настроений# распознавание объектов# обобщение документов# обнаружение тем# самостоятельный поиск# контролируемое обучение# обучение с нуля.# attention mechanisms# word embeddings# text classification# entity recognition# document summarization# topic detection# machine translation# self-supervised learning# zero-shot learning.

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Article Identifiers

ROI:

https://eroi.uz/11.0155/A0125-0019

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Not Available

References

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