NLP Landscape from the 1960s to the 2020s

I have just started learning NLP, so while going through the Introduction to NLP, I realized that the field of natural language processing (NLP) had evolved significantly since its inception in the 1960s.

What is NLP?

NLP is a subfield of linguistics (human language), computer science, and artificial intelligence. Real-world applications include spam filtering, removing adult content, search engines, chatbots, and smart replies.

Here is a brief overview of the NLP landscape:

1960s-1970s:

• The early years of NLP saw a focus on rule-based approaches that relied heavily on hand-crafted linguistic rules.
• The development of the Chomsky hierarchy of formal languages and the use of context-free grammar for parsing sentences laid the foundation for many early NLP systems.
• One of the first successful NLP applications was the ELIZA chatbot, which used simple pattern matching and substitution to simulate a conversation with a psychotherapist.

1980s-1990s:

• This period saw the emergence of statistical methods for NLP, which gradually began to replace rule-based approaches.
• The introduction of machine learning algorithms, such as hidden Markov models and Naive Bayes classifiers, enabled more sophisticated text analysis tasks such as part-of-speech tagging and named entity recognition.
• The development of large-scale annotated corpora, such as the Penn Treebank, enabled the training of more accurate statistical models.

2000s-2010s:

• The rise of the internet and social media led to an explosion of textual data, which in turn spurred the development of new NLP techniques.
• The development of distributed word representations, such as word2vec and GloVe, revolutionized NLP by enabling the use of neural network architectures for language processing tasks.
• Deep learning techniques, such as convolutional neural networks and recurrent neural networks, became popular for a wide range of NLP tasks, including sentiment analysis, machine translation, and question-answering.

2020s:

• The current state of NLP is characterized by the use of large-scale pre-trained language models, such as BERT and GPT-3, which have achieved state-of-the-art performance on a wide range of language processing tasks.
• These models are trained on massive amounts of text data and can be fine-tuned for specific NLP tasks, making them highly versatile.
• The development of novel techniques such as transformer-based models has enabled even more powerful language models, with GPT-3 being the largest model developed so far.

I know this journey is going to be really hard for me, but I am really excited to learn about what is coming next.