Natural Language Processing Introduction: Getting Started with NLTK

Foundations of Natural Language Processing (NLP)

In this section we will be covering Natural Language Processing (NLP), which refers to analytics tasks that deal with natural human language, in the form of text or speech.

Natural Language Tool Kit (NLTK)

We'll start by providing more context on the Natural Language Tool Kit (NLTK), one of the most popular NLP libraries used in Python. This library was developed by researchers at the University of Pennsylvania, and it has quickly become one of the most powerful and complete library of NLP tools available.

Regular Expressions

Data preprocessing is an essential part of NLP, and that's why being very familiar with regular expressions is extremely important. Regular expressions, or "Regex" is extremely useful for NLP. We can use regex to quickly pattern match and filter through text documents.

Feature Engineering for Text Data

Working with text data comes with a lot of ambiguity. Feature engineering for NLP is pretty specific, and in this section you'll learn some feature engineering techniques that are essential when working with text data. You'll learn how to remove stop words from your text, as well as how to create frequency distributions, representing histograms that give us an overview of the total number of times each word occurs in a given text corpus.

Additionally, you'll learn about stemming and lemmatization, which is the technique of removing suffixes from our words (and can enhance our text insight by creating frequency histograms after having performed stemming or lemmatization!). You'll also learn how to create bigrams, which creates an insight on how often two words occur together!

Context-Free Grammars and Part-of-Speech (POS) Tagging

In NLP, it is important to understand what context-free grammars and part-of-speech tagging are. Context-free grammars refer to bits of text that are grammatically correct, but feel like complete nonsense when considering the same bit of text on the semantic level. POS tagging refers to the act of helping a computer understand how to interpret a sentence. The context-free grammars (CFG) defines the rules of how sentences can exist.

You'll see multiple examples on how to use both CFG and POS tagging, and why they are important!

Text Classification

We will finish off this section by explaining the general process to set text data up for classification problems.

Introduction to NLP with NLTK

Here, we'll discuss a general overview of Natural Language Processing, and the popular Python library for NLP, Natural Language Tool Kit (NLTK).

What is Natural Language Processing?

Natural Language Processing, or NLP, refers to analytics tasks that deal with natural human language, in the form of text or speech. These tasks usually involve some sort of machine learning, whether for text classification or for feature generation, but NLP isn't just machine learning. Tasks such as text preprocessing and cleaning also fall under the NLP umbrella.

The most common Python library used for NLP tasks is Natural Language Tool Kit, or NLTK for short. This library was developed by researchers at the University of Pennsylvania, and quickly became the most powerful and complete library of NLP tools available.

Using NLTK

NLTK is a sort of "one-stop shop" for all things NLP. It contains many sample corpora, with everything from full texts from Project Gutenberg to transcripts of State of the Union speeches from US Presidents. This library contains functions and tools for everything from data cleaning and preprocessing, to linguistic analysis, to feature generation and extraction. NLTK even contains its own Bayesian classifiers for quick testing (although realistically, you'll likely want to continue using scikit-learn for these sorts of tasks).

NLP is unique in that in addition to statistics and math, it also relies heavily on the field of Linguistics. Many of the concepts you'll run into will be grounded in linguistics. Some of them will seem a bit foreign to you if you haven't studied languages or grammar yet, but don't worry! The reality of it all is that you don't need deep expertise in linguistics to work with text data, because NLTK was built by professionals to make it easier for everyone to access the linguistic tools and methods needed for working with text data.

Although a linguist knows how to manually generate something like a Parse Tree for a sentence, NLTK provides this functionality for you in just a few lines of code.

Working With Text, Simplified

Generally, projects that work with text data follow the same overall pattern as any other projects. The main difference is that text projects usually require a bit more cleaning and preprocessing than regular data, in order to get the text into a format that's usable for modeling.

Here are some of the ways that NLTK can make our lives easier when working with text data:

• Stop Word Removal: NLTK contains a full library of stop words, making it easy to remove the words that don't matter from our data.
• Filtering and Cleaning: NLTK provides simple, easy ways to create and filter frequency distributions, as well providing multiple ways to clean, stem, lemmatize, or tokenize datasets.
• Feature Selection and Feature Engineering: NLTK contains tools to quickly generate features such as bigrams and n-grams. It also contains major libraries such as the Penn Tree Bank to allow quick feature engineering, such as generating part-of-speech tags, or sentence polarity.

With effective use of NLTK, we can quickly process and work with text data, allowing us to quickly get our data into the shape needed for tasks we're familiar with, such as classification!

Key NLP Concepts and Techniques

Let's dive deeper into some of the fundamental concepts and techniques you'll encounter when working with NLP:

Text Preprocessing Pipeline

A typical NLP preprocessing pipeline includes several essential steps:

1. Tokenization: Breaking text into individual words or tokens
2. Lowercasing: Converting all text to lowercase for consistency
3. Stop Word Removal: Removing common words like "the", "and", "is" that don't carry much meaning
4. Punctuation Removal: Cleaning out punctuation marks that may not be relevant
5. Stemming/Lemmatization: Reducing words to their root forms

Stemming vs. Lemmatization

Both techniques aim to reduce words to their base forms, but they work differently:

• Stemming: Removes suffixes using simple rules (e.g., "running" → "run", "better" → "better")
• Lemmatization: Uses vocabulary and morphological analysis to return the dictionary form (e.g., "running" → "run", "better" → "good")

N-grams and Feature Extraction

N-grams are contiguous sequences of n items from text:

• Unigrams: Individual words ("natural", "language", "processing")
• Bigrams: Two-word combinations ("natural language", "language processing")
• Trigrams: Three-word combinations ("natural language processing")

These n-grams help capture context and relationships between words, which is crucial for understanding meaning in text.

Practical Applications of NLP

NLP has numerous real-world applications across various industries:

Text Classification

• Spam email detection
• Sentiment analysis of customer reviews
• Document categorization
• News article classification

Information Extraction

• Named Entity Recognition (NER)
• Relationship extraction
• Key phrase extraction
• Topic modeling

Language Generation

• Chatbots and virtual assistants
• Automated report generation
• Content summarization
• Machine translation

Getting Started with NLTK

To begin working with NLTK, you'll need to install it and download the necessary data:

Once installed, NLTK provides a comprehensive suite of tools that make text processing tasks much more manageable. Whether you're building a sentiment analysis system, creating a chatbot, or analyzing customer feedback, NLTK provides the foundational tools you need to succeed.

Next Steps in Your NLP Journey

As you continue learning NLP, consider exploring these advanced topics:

• Word embeddings (Word2Vec, GloVe)
• Deep learning for NLP (RNNs, LSTMs, Transformers)
• Advanced text classification techniques
• Named Entity Recognition and dependency parsing
• Modern language models (BERT, GPT)

Remember, NLP is a rapidly evolving field with new techniques and models being developed regularly. The fundamentals covered in this introduction will provide you with a solid foundation to build upon as you explore more advanced topics and applications.