<ul data-eligibleForWebStory="true"><li>Key phases in NLP evolution include:</li><li>1. Early 2010s - Statistical Methods</li><li>2. Mid 2010s - Deep Learning Era</li><li>3. 2017 - The Transformer Revolution</li><li>4. 2020-2025 - Large Language Models (LLMs)</li><li>The need for NLP arises due to its ability to bridge the gap between human language and computer understanding, with real-world applications and various tasks.</li><li>Common approaches to NLP involve Regular Expression, Wordnet, Open Mind Common Sense, Machine Learning-based methods like Navies Bayes, Logistic Regression, SVM, LDA, and Deep Learning-based approaches using architectures like RNN, LSTM, GRU/CNN, Transformer, and Autoencoder.</li><li>NLP faces challenges in transitioning from basic language rules to achieving human-like conversations, leading to the emergence of more intelligent and context-aware systems.</li></ul>

Tracing the NLP Revolution: A Decade of Language and Learning

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