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20.2 Historical Evolution of NLP

20.2 Historical Evolution of NLP#

20.2.1 Early Rule-Based Systems (1950s–1980s)#

NLP began in the 1950s with a focus on symbolic approaches. Early researchers developed rule-based systems where grammar rules were manually coded. For example, machine translation systems like the Georgetown-IBM experiment (1954) translated a few Russian sentences into English using a small set of linguistic rules.

Challenges:

  • High maintenance effort

  • Poor adaptability to new domains

  • Limited understanding of context

20.2.2 Statistical NLP (1990s–2010s)#

With the availability of large datasets and increased computational power, NLP shifted toward statistical models. Probabilistic approaches, such as n-gram language models, Bayesian classifiers, and Hidden Markov Models (HMMs), became popular. These methods learned patterns from text data instead of relying solely on hand-written rules.

Key advantages:

  • Better scalability

  • Adaptation to multiple domains

  • Limitations remained in handling long-term dependencies and semantic understanding.

20.2.3 Neural NLP and Deep Learning Era (2013–Present)#

The advent of deep learning revolutionized NLP. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks improved the modeling of sequential data. Later, transformer architectures—introduced in “Attention Is All You Need” (2017)—enabled massive improvements in tasks like translation, summarization, and question answering. Pre-trained language models such as BERT, GPT, and T5 have since set new benchmarks across nearly all NLP tasks.

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