Introduction to Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that lies at the intersection of Computer Science, Artificial Intelligence, and Linguistics.

It focuses on enabling machines to understand, interpret, and interact with humans using natural language.

This approach is fundamentally different from traditional programming, where we rely on formal and structured languages.

Real-World Applications of NLP

1. Text / Document Classification

Consider a real-world example where you raise an issue related to a water filter through a mobile app or website.

The system automatically classifies the issue based on priority, such as high or low.

If the issue is high priority, it is monitored by a dedicated support or escalation team, which enables instant communication with the execution team.

If the issue is low priority, you may receive an acknowledgment message, and the issue may be resolved within a longer timeframe (for example, a week).

This automatic prioritization is achieved using NLP-based text classification techniques.

2. Chatbots

Chatbots are one of the most impactful and visible applications of NLP today and are widely used across industries.

Chatbot evolution can be broadly categorized into:

Rule-based chatbots

These respond only with predefined answers to specific queries.

Flow-based chatbots

These can store limited context and follow a structured conversational flow.

Open-ended (AI-powered) chatbots

These use advanced NLP and deep learning models, enabling conversations that closely resemble human interaction.

3. Search Engines

Search engines efficiently perform both information retrieval and information extraction.

Information Retrieval: Retrieves and ranks relevant web pages from highest to lowest relevance.

Information Extraction: Extracts key information from documents and presents it in a summarized format. Techniques such as regular expressions (regex) can be used for structured extraction.

Modern search engines like Google use Transformer-based models (such as BERT) to better understand user intent and contextual meaning.

4. Machine Translation

Machine translation systems (e.g., Google Translate) automatically:

Detect the source language,

Understand its semantic structure,

Translate it into the target language.

Modern translation systems are primarily based on encoder–decoder architectures using transformers, rather than traditional autoencoders.

5. Other NLP Applications

Other common NLP use cases include:

Text summarization

Text generation

Sentiment analysis

Knowledge graphs

Speech-to-text

Spell checking

Approaches to Solving NLP Problems

There are three main approaches to solving NLP problems:

1. Heuristic (Rule-Based) Approach

This approach relies on manually defined rules and domain knowledge.

Common techniques include:

Regex — to extract specific patterns

WordNet — a lexical dictionary available in Python

Open Mind Common Sense — a database of common-sense facts

This is often the first approach and is reliable in many cases because rules are developed with the help of domain experts. However, we cannot create rules for every open-ended problem.

2. Machine Learning-Based Approach

Machine Learning (ML) models learn patterns directly from data instead of relying on handcrafted rules.

Common ML algorithms used in NLP include:

Naive Bayes

Support Vector Machines (SVM)

Logistic Regression

Latent Dirichlet Allocation (LDA)

Hidden Markov Models (HMM)

Limitation:

While ML models are interpretable, they often fail to preserve word order and sentence structure due to vectorization techniques like Bag-of-Words or TF-IDF.

3. Deep Learning–Based Approach

To overcome this limitation, we use DL-based approaches.

DL models retain sentence structure and use representation learning, meaning they automatically learn the best features for the problem.

Common DL models used in NLP include:

RNN

LSTM

GRU

CNN

Autoencoders

Transformers (the main reason behind the evolution of Generative AI)

No approach is outdated. Based on the scenario and problem requirements, we choose the most suitable approach.

Pre-trained Models and Libraries

We have multiple libraries and models contributed by top companies like Facebook and Google, such as FastText, TensorFlow Hub, and GPT-3, which allow us to use pre-trained models through transfer learning and fine-tune them for specific problems.

Challenges of NLP

Even though NLP has many advantages, it also has some challenges:

Difficulty in understanding ambiguity, double-meaning sentences, poetry, idioms, colloquialisms, and creativity.

“I saw a boy on the beach with my binoculars.”

Contextual word meanings

“I ran to the store because I ran out of milk.”

Idiomatic expressions

“Piece of cake.”