Get Started with NLP The most comprehensive NLP guide. From its origin story & use cases to tutorials to get you started.

What is Natural Language Processing (NLP)

Natural Language Processing (NLP) is a technology used by computers to understand, interpret, and generate human language. Essentially, it helps computers read, decipher, respond to, and even mimic the way humans speak and write. NLP is used in everyday applications like voice-activated GPS systems, digital assistants (like Siri and Alexa), and in tools that manage emails or analyze large volumes of text. Its goal is to make interactions between humans and machines easier and more natural.

It is a fascinating field that sits at the crossroads of computer science, artificial intelligence (AI), and linguistics. It involves the use of algorithms to understand and generate human language in a way that is both meaningful and useful. NLP enables computers to interact with humans in their own language and to perform tasks like translation, sentiment analysis, and more.

The Origins of NLP: A Historical Perspective

The story of NLP begins in the 1950s, a golden era of curiosity about human-computer interaction. Pioneers like Alan Turing, who proposed the Turing Test as a measure of machine intelligence, set the stage for what would become the field of artificial intelligence. However, the first major NLP application was machine translation, notably the Georgetown experiment in 1954 which involved fully automatic translation of more than sixty Russian sentences into English.

As the decades progressed, the focus shifted from rule-based methods of the early days to statistical methods in the 1980s and 1990s. These methods utilized large amounts of text data to learn from patterns in language, marking a significant shift towards data-driven approaches.

Read up on John Grinder & Richard Bandler

John Grinder and Richard Bandler are pivotal figures in the development of Neuro-Linguistic Programming (NLP), a methodology distinct from computational linguistics but influencing areas of natural language understanding (NLU) and human behavior modeling.

While not directly related to the computational side, their work laid foundational concepts that echo in NLU tasks like summarization, question answering, and word sense disambiguation. Their exploration into the structured frameworks of human communication has metaphorical parallels in machine learning models used in NLP techniques, where syntactic and semantic analysis plays crucial roles.

Although their primary focus wasn’t on developing technologies like speech recognition or speech-to-text, the principles of pattern recognition and transformation in neuro-linguistic programming provide a philosophical backbone to modern AI endeavors in generative AI, embeddings, and APIs that enhance interactions between computational systems and human linguistic input.

As such, their theoretical contributions indirectly support the development of tools for enhanced machine-human interaction, facilitating use cases across digital platforms, including search engines and advanced NLG systems.

The Rise of Machine Learning and Deep Learning in NLP

With the advent of the internet and the explosion of digital text, such as social media, NLP began to leverage machine learning—a subset of AI that studies the construction of algorithms that can learn from and make predictions on data. By the 2000s, deep learning had revolutionized NLP by using neural networks that mimic human brain functions, enabling significant advancements in language modeling and processing.

The DNA of NLP

1. Syntax and Semantics: Syntax refers to the arrangement of words to create meaningful sentences. Semantics, on the other hand, is about the meaning behind those words. Understanding both is crucial for effective NLP.
2. Tokenization and Part-of-Speech Tagging: Tokenization is the process of breaking down text into tokens (like words), while part-of-speech tagging assigns those words to their respective parts of speech (like noun, verb, etc.).
3. Named Entity Recognition and Sentiment Analysis: These tasks help identify names of people, organizations, or locations in text, and gauge the sentiment expressed in the text, which is especially useful in monitoring social media.
4. Machine Translation and Natural Language Generation: These are complex fields that convert text from one language to another and generate coherent text, respectively.
5. Neural Networks and Deep Learning: These technologies underpin many state-of-the-art NLP models that automate and optimize various linguistic tasks.

NLP in the Real World: Applications Across Industries

1. Healthcare: NLP is used to sift through unstructured clinical notes for valuable insights, automate patient interaction through chatbots, and more.
2. Finance: Sentiment analysis helps gauge market sentiment from financial news and social media.
3. Customer Service: Chatbots and virtual assistants use NLP to provide and support real-time customer interaction without human intervention.
4. Sentiment Analysis: Businesses leverage sentiment analysis to monitor and analyze opinions and feelings expressed in social media posts, reviews, and other text about their products or services. This helps companies understand public sentiment, manage brand reputation, and develop product strategies.
5. Machine Translation: Services like Google Translate employ NLP to provide instant translation of text and speech between multiple languages. This technology is vital for global communication, allowing people to interact and access information across language barriers.
6. Email Filtering: NLP is used in spam filters to analyze the content of emails to detect and block spam. More sophisticated systems also categorize emails into folders such as social, promotions, and primary, helping users organize their inboxes more efficiently.
7. Voice-Activated GPS Systems: Modern GPS systems use NLP to understand spoken directions and place names, making it easier for drivers to navigate without having to type or look at a screen.
8. 6. Content Recommendations: Streaming services like Netflix and Spotify use NLP to analyze your interactions and preferences to recommend movies, shows, or music tailored to your tastes.8. Legal Document Analysis: Law firms use NLP to sift through large volumes of legal documents to identify relevant information, saving time and reducing the manual effort required in pre-trial research.9. Resume Filtering: HR departments use NLP tools to scan and filter job applications more efficiently, identifying the most promising candidates based on their relevance to the job descriptions.

Getting started with NLP & AI

Python has emerged as the leading programming language for NLP thanks to libraries like NLTK (Natural Language Toolkit), SpaCy, and TensorFlow. These open-source tools offer ready-to-use methods for most NLP tasks including parsing, stemming, lemmatization, and building neural networks.

Programming languages for NLP & AI you should become familiar with

1. Python: Widely regarded as the leading language for AI and NLP projects due to its simplicity and readability, Python boasts a rich ecosystem of libraries and frameworks such as TensorFlow, PyTorch, NLTK, and SpaCy. These tools offer extensive functionalities for data manipulation, machine learning, and NLP, making Python a top choice for developers and researchers.
2. Java: Known for its speed, scalability, and robustness, Java is another popular choice, especially in large, complex enterprise environments. Libraries like Deeplearning4j, Stanford NLP, and OpenNLP make Java a strong contender for implementing AI and NLP solutions that require high performance and scalability.
3. R: While R is traditionally used for statistical analysis and data visualization, it also has packages like ‘text’, ‘tm’ (Text Mining), and ‘word2vec’ that are useful for NLP. R is particularly favored by data scientists for exploratory data analysis, making it valuable for tasks that involve heavy data manipulation and analysis in NLP.
4. C++: While not as popular for rapid development of NLP applications, C++ is used in scenarios where performance and memory management are critical. It’s often used in the development of AI and NLP frameworks and tools that Python and other languages rely on under the hood.
5. JavaScript: With the rise of platforms like Node.js, JavaScript has become a viable option for building lightweight, real-time NLP applications. Libraries like Natural and TensorFlow.js enable developers to integrate AI and NLP functionalities directly into web applications, making it a practical choice for interactive web apps.

Getting started with Python for NLP and AI.

Install the libraries

First, you need to install NLTK and SpaCy. You can do this using pip:

pip install nltk spacy

For SpaCy, you also need to download a language model. Here, we use the English model:

python -m spacy download en_core_web_sm

Basic NLP Tasks with NLTK and SpaCy

Here’s a Python script that demonstrates basic NLP tasks:

import nltk
from nltk.tokenize import word_tokenize
from nltk import pos_tag
from nltk.corpus import stopwords

import spacy

# Ensure NLTK resources are downloaded (e.g., tokenizers, stopwords)
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('stopwords')

# Sample text
text = "Apple Inc. is an American multinational technology company headquartered in Cupertino, California."

# Tokenization and POS Tagging with NLTK
tokens = word_tokenize(text)
pos_tags = pos_tag(tokens)

print("Tokenization and POS Tagging with NLTK:")
print(pos_tags)

# Filtering Stopwords
stop_words = set(stopwords.words('english'))
filtered_tokens = [w for w in tokens if not w.lower() in stop_words]

print("\nFiltered Tokens (Stopwords Removed):")
print(filtered_tokens)

# Initializing SpaCy and performing Named Entity Recognition
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)

print("\nNamed Entity Recognition with SpaCy:")
for entity in doc.ents:
    print(f"{entity.text} ({entity.label_})")

1. Tokenization and POS Tagging (NLTK): The script uses NLTK to tokenize the text into words and then tags each token with its part-of-speech. This can be helpful for understanding the grammatical structure of sentences.
2. Stopwords Removal: Stopwords (common words like ‘the’, ‘is’, etc.) are often filtered out during text processing to focus on more meaningful words. The script demonstrates how to remove these from the tokenized text.
3. Named Entity Recognition (SpaCy): Using SpaCy, the script performs named entity recognition to identify and classify names of things (e.g., companies, locations) within the text. This is useful for extracting specific types of information from text, like company names or geographic locations.

Best NLP Tutorials

For those interested in diving into NLP, countless resources range from online courses to textbooks. Tutorials often start with basic concepts and gradually introduce more sophisticated techniques and tools, helping both beginners and experienced programmers.

1. Coursera – One standout course is “Natural Language Processing” by the National Research University Higher School of Economics. It covers a broad range of topics from text processing and tagging to sentiment analysis and dialogue state tracking.
2. Fast.ai – Known for its practical approach and deep learning focus, fast.ai offers an NLP course that is project-oriented and uses modern libraries and tools. This course is particularly good if you want hands-on experience and prefer to learn by doing. It’s also completely free and includes video lectures along with interactive Jupyter notebooks.
3. Stanford Online – Stanford University offers a course called “Natural Language Processing with Deep Learning”. It is taught by leading AI researchers and professors such as Christopher Manning.

As we move further into the age of AI, NLP continues to grow at an unprecedented rate. Large language models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have set new benchmarks in the field. With ongoing research and the increasing availability of extensive datasets, the potential for NLP to understand and generate human language more effectively is boundless.

In essence, natural language processing connects the dots between computer understanding and human communication, making it a cornerstone of today’s AI technology landscape. Whether it’s through sophisticated chatbots, insightful sentiment analysis, or groundbreaking machine translation, NLP remains at the forefront of technological advances that are reshaping our world.