Natural Language Processing with spaCy & NLTK in Python

Introduction to NLP

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What is NLP?

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Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps machines understand, interpret, and respond to human language just like we do when we talk, write, or text.

Think of it as the bridge between human communication (language) and computer understanding (code).

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Why does NLP matter in today's tech world?

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In the modern digital age, we generate tons of text and voice data every second through emails, chats, social media, customer reviews, call centres, and more.

• Understanding Big Data (Text): NLP helps organisations analyse millions of texts (like tweets, emails, reviews) to extract useful insights.  
• Building Smart AI Systems: From chatbots to virtual assistants like Siri or Alexa, NLP powers machines to talk to humans.
• Breaking Language Barriers: NLP enables real-time translation between languages (like Google Translate), making the world more connected.
• Making Search Smarter: Search engines like Google use NLP to understand what you mean (not just what you typed).

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Real-Time Use Cases of NLP

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It’s working behind the scenes in the everyday apps we use. From customer service to healthcare, NLP is transforming how humans interact with technology.  

• Chatbots & Virtual Assistants: customer support bots, Siri, Alexa, Google Assistant
• Sentiment Analysis: Social media monitoring, Product review analysis
• Language Translation: Google Translate, YouTube captions  
• Medical and Legal Document Analysis: Hospitals (patient record analysis), Law firms (contract reviews)

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Meet the Contenders: spaCy vs NLTK

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When you start learning Natural Language Processing (NLP) in Python, two big names appear first:

• NLTK (Natural Language Toolkit)
• spaCy  

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What is NLTK?

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NLTK (Natural Language Toolkit) is one of the oldest and most popular NLP libraries in Python.

Best known for:

• Teaching and research
• Tons of linguistic datasets (corpora)
• Wide range of NLP tasks: tokenization, stemming, tagging, parsing

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What is spaCy?

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spaCy is a modern, fast, and production-ready NLP library.

Best known for:

• Speed and performance
• Pre-trained pipelines
• Ease of use in real-world apps

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Installation Steps

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Install NLTK

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Install NLTK using pip command,

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To download all resources (like corpora and tokenizers) using this command,

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Install spaCy

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Install spaCy using this command,

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To download a pre-trained English model:

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And use it like this:

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Tokenization in NLP

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Tokenization is the process of splitting text into smaller parts, usually words or sentences. It’s the very first and essential step in any NLP pipeline.

Let’s see how spaCy and NLTK handle this with a simple example:

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Using NLT

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• NLTK splits punctuation into separate tokens.
• Treats contractions like “I’m” as ["I", "'m"].
• Good for traditional token-level analysis.

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Using spaCy

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• spaCy gives similar output, but it's optimized for speed and scalability.
• Also provides extra info with each token (e.g., POS tag, lemma, entity).

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Part-of-Speech (POS) Tagging in NLP

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Part-of-Speech tagging means labeling each word in a sentence with its grammatical role like noun, verb, adjective, etc.

This helps machines understand the structure and meaning of sentences.

Let’s compare how spaCy and NLTK perform POS tagging with simple example:

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Using NLTK

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• Uses Penn Treebank tag set.
• Requires manual downloading of taggers.
• Suitable for basic syntactic parsing.

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Using spaCy

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• Combines universal and fine-grained tags.
• Runs faster and cleaner.
• Easily integrates with other features like named entity recognition.

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Named Entity Recognition (NER) in NLP

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Named Entity Recognition (NER) is the process of identifying real-world objects (like names of people, companies, places, dates, money, etc.) in a text and classifying them into predefined categories.

Let’s compare how spaCy and NLTK perform NER with simple example:

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Using NLTK

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• NLTK uses shallow parsing (chunking).
• Accuracy is lower than spaCy.
• No built-in support for newer entity types (like MONEY, PRODUCT, TIME).
• Slower and less suitable for real-time tasks.

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Using spaCy

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• ent.text: The actual entity
• ent.label_: The type (e.g., PERSON, ORG, GPE - Geo-Political Entity)
• spaCy has a built-in NER model and gives very high accuracy with speed.

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NOTE:

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While spaCy is generally more accurate and faster, it's not always correct. Its small model (en_core_web_sm) may misclassify entities due to limited training data or ambiguous context. For example, it might label a city like Coimbatore as an organization if the sentence structure suggests it.

In contrast, NLTK uses rule-based methods (POS tagging + regex chunks), which can be more reliable in clearly defined cases.

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Stop Words Removal

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Removing common words like "is," "and," "the" that add little meaning.  Cleans up text for better analysis and reduces noise.

Let’s compare how spaCy and NLTK perform Stop word removal with simple example:

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Using NLTK

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NLTK requires downloading the stop word list and handles removal manually using lists and token filters.

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Using spaCy

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spaCy has a built-in list of stop words and checks each token using .is_stop.

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Lemmatization

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Lemmatization is the process of converting a word to its base or dictionary form (called a lemma), while considering its part of speech (POS) and context.

For example:

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• "running" → "run"
• "better" → "good"

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Using NLTK

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With POS tagging, NLTK gives very similar results to spaCy.

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Using spaCy

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spaCy automatically detects POS and gives accurate lemmatization.

Stemming in NLP

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Stemming is the process of chopping off prefixes or suffixes to reduce a word to its root form.

For example:

• "playing" → "play"
• "played" → "play"
• "faster" → "fast"

Unlike lemmatization, stemming does not consider grammar or context, it’s rule-based and often aggressive.

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Using NLTK

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It reduces "running" to "run" and "runs" to "run".

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Using spaCy

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spaCy does NOT have a built-in stemmer.  It focuses on lemmatization, which is more accurate and context-aware.

If you want to stem in spaCy, you can use an external stemmer like NLTK’s with it:

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Feature-by-Feature Comparison

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Pros & Cons of spaCy vs NLTK

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spaCy

Pros

• Fast & efficient optimized for performance.
• Pretrained models for POS, NER, parsing, etc.
• High accuracy in real-world use cases.
• Pipeline integration for clean NLP workflows.
• Supports multiple languages.  

Cons

• Not beginner-friendly, steep learning curve for new users.
• Harder to customize.
• Small models (e.g., en_core_web_sm) may miss rare entities or make wrong predictions.

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NLTK

Pros

• Beginner-friendly — ideal for learning and teaching.
• Highly customizable — great for rule-based NLP.
• Comes with a huge library of datasets and corpora.
• Useful for linguistic experimentation, regex, grammar rules, etc.  

Cons

• Slower — not optimized for large-scale processing.
• Not production-ready — lacks speed and pretrained models

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Conclusion

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Both spaCy and NLTK are powerful NLP tools, but they serve different purposes:

• Use spaCy when you want speed, accuracy, and ready-to-use models for real-world applications.
• Use NLTK when you're learning, doing rule-based tasks, or need more custom control.

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