Roadmap to Learn Natural Language Processing in 2023

Photo of Alexa Device by Andres Urena on Unsplash

Learning Natural Language Processing (NLP) can be a rewarding journey, but it can also be complex due to its multidisciplinary nature. Here’s a roadmap to help you get started and progress in your NLP learning journey

Prerequisites

1. Basics of Python: Start by learning Python, as it is the primary language for NLP libraries and tools. You can use resources like Codecademy, Python.org, or “Python Crash Course” by Eric Matthes, or our all-free resource of the decade YouTube.
2. Fundamentals of Machine Learning: Understand machine learning concepts such as supervised learning, unsupervised learning, and evaluation metrics. Familiarize yourself with libraries like scikit-learn for basic ML tasks.
3. Understanding of Deep Learning: Understanding neural network basics, layers, and activation functions. Familiarity with optimization algorithms like SGD, Adam, and RMSprop. Knowledge of common loss functions used in NLP tasks.

Why NLP?

Natural Language Processing (NLP) is a valuable area of study from a learning perspective because it bridges the gap between human communication and machines hence unlocking the power of words. It enables us to teach computers to understand, generate, and interact with human language. Learning NLP equips individuals with skills to analyze vast amounts of textual data, build intelligent chatbots, automate language-related tasks, and contribute to groundbreaking advancements in fields like artificial intelligence and linguistics.

Steps to unlock the power of words

Step 1: Text Cleaning

These techniques represent manual practices aimed at optimizing our text data for improved model performance. Let’s delve into them with a more detailed understanding:

1. Mapping and Replacement: This involves mapping words to standardized language equivalents. For instance, words like “b4” and “ttyl,” commonly understood by humans as “before” and “talk to you later,” pose challenges for machines. Normalization entails mapping such words to their standardized counterparts.
2. Correction of Typos: Written text often contains errors, such as “Fen” instead of “Fan.” To rectify these errors, a dictionary is employed to map words to their correct forms based on similarity. This process is known as typo correction.

It’s worth noting that these are just a few of the techniques discussed here, and staying updated with various methods is essential for continual learning and improvement.

Step 2: Text Preprocessing Level-1

Textual data that isn’t directly compatible with Machine Learning algorithms. Therefore, our initial task involves preprocessing this data before feeding it into our Machine Learning models. This step aims to familiarize ourselves with the fundamental processing techniques essential for tackling nearly every NLP challenge. Techniques such as Tokenization, Lemmatization, Stemming, Parts of Speech (POS), Stopwords removal, and Punctuation removal are used.

Image from hex.tech

Step 3: Text Preprocessing Level-2

In this phase, we explore fundamental techniques for transforming our textual data into numerical vectors, making it suitable for Machine Learning algorithms. These techniques include:

1. Bag of Words (BOW): This method represents text by creating a “bag” of individual words, disregarding their order but considering their frequency. Each word is treated as a feature, and the count of each word in a document is used for vectorization.
2. Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF calculates the importance of words in a document relative to a collection of documents. It assigns higher weights to words that are more specific to a document and less common across the entire collection.
3. Unigram, Bigram, and Ngrams: These techniques involve considering single words (unigrams), pairs of consecutive words (bigrams), or groups of N consecutive words (N-grams) as features for vectorization. They capture different levels of context and can be useful for various NLP tasks.

These methods are essential for converting text data into a format that Machine Learning algorithms can effectively process and analyze.

Step 4: Text Preprocessing Level-3

At this stage, we delve into advanced techniques for converting words into vectors, enhancing our ability to represent and analyze textual data:

1. Word2Vec: Word2Vec is a state-of-the-art word embedding technique that transforms words into dense vector representations in a way that captures semantic relationships i.e., the relation of the words, in the context, with other words. It considers the context in which words appear, allowing words with similar meanings to have similar vector representations.
2. Average Word2Vec: This technique builds upon Word2Vec by averaging the vector representations of words in a document. It creates a document-level vector that retains semantic information from individual words.

Image by Ruben Winastwan

These advanced methods empower us to represent text data in a more meaningful and context-aware manner, enabling improved performance in various Natural Language Processing tasks.

Step 5: Hands-on Experience on a use case

Having completed the preceding steps, it’s time to put our knowledge into practice by tackling a typical or straightforward NLP use case. This hands-on experience involves implementing machine learning algorithms such as the Naive Bayes or Support Vector Machine Classifier. By doing so, we gain a practical understanding of the concepts covered thus far, providing a solid foundation for comprehending the subsequent stages of our NLP journey. We’ll be covering a project with the tools and techniques we have learned this far.

Step 6: Exploring Deep Learning Models

In this step, we now start exploring deep learning models for Natural Language Processing (NLP), gaining insights into their core architectures:

P.S. You need to know an advanced level understanding of Artificial Neural Network

1. Recurrent Neural Networks (RNN): RNNs are particularly valuable when dealing with sequential data. They allow us to analyze data with a temporal sequence, making them highly relevant for NLP tasks involving text or speech.
2. Long Short-Term Memory (LSTM): LSTM is an advanced variation of RNN designed to handle the vanishing gradient problem and capture long-term dependencies in sequential data. It’s especially well-suited for NLP tasks demanding memory of context over extended sequences.
3. Gated Recurrent Unit (GRU): Similar to LSTM, GRU is another variant of RNN designed to address certain computational complexities. It is efficient and effective for modeling sequential data in NLP tasks.

Architecture of LSTM

Understanding these deep learning models is crucial for more advanced NLP applications and lays the foundation for grasping subsequent concepts in the NLP learning journey.

Step 7: Advanced Text Preprocessing

At this stage, we’ll start using advanced text preprocessing techniques such as Word Embedding and Word2Vec that will empower us to tackle moderate-level projects in the field of Natural Language Processing (NLP) and establish ourselves as proficient practitioners:

By mastering these advanced preprocessing techniques, we gain a competitive edge and the ability to undertake more complex NLP projects, solidifying our expertise in this domain.

Step 8: Exploring Advanced NLP Architectures

In this step, we delve into advanced NLP architectural components that expand our understanding of deep learning and its applications in NLP:

1. Bidirectional LSTM RNN: Bidirectional LSTM (Long Short-Term Memory) RNNs enhance sequential data analysis by processing data in both forward and backward directions. This bidirectional approach captures richer context and dependencies, making it invaluable for advanced NLP tasks.
2. Encoders and Decoders: Encoders and decoders are critical components of sequence-to-sequence models, commonly used in tasks like machine translation and text summarization. Understanding these components allows us to work on complex NLP tasks involving structured transformations of text.
3. Self-Attention Models: Self-attention models, exemplified by the Transformer architecture, are revolutionizing NLP. They excel at capturing long-range dependencies and contextual information, making them the backbone of models like BERT. Proficiency in self-attention mechanisms is vital for modern NLP.

By grasping these advanced architectural elements, we’ll be well-equipped to tackle sophisticated NLP challenges and leverage cutting-edge techniques to enhance our NLP projects.

Step 9: Mastering Transformers

In this step, we focus on mastering the Transformer architecture, a pivotal advancement in Natural Language Processing (NLP). Transformers are a groundbreaking architecture designed to address sequence-to-sequence tasks while efficiently handling long-range relationships within text data. They achieve this by leveraging self-attention models.

Understanding Transformers is essential for staying at the forefront of NLP developments and effectively harnessing their capabilities for tasks like language translation, text generation, and question-answering systems. Mastery of Transformers marks a significant milestone in our NLP journey and we’ll be able to cover most of the used cases effectively.

Step 10: Mastering Advanced Transformer Models

In this step, we delve into advanced Transformer models, including:

1. BERT (Bidirectional Encoder Representations from Transformers): BERT is a remarkable variation of the Transformer architecture. It excels at converting sentences into vectors and is widely used for natural language processing pre-training tasks. Understanding BERT is pivotal for tackling a wide range of NLP challenges with state-of-the-art performance.
2. GPT (Generative Pre-trained Transformer): GPT is another powerful transformer-based model known for its language generation capabilities. It’s widely employed in tasks like text generation, question-answering, and more.

Comprehending these advanced Transformer models enhances our NLP expertise, enabling us to excel in a variety of NLP applications and stay up-to-date with the latest advancements in the field.

While this NLP roadmap may seem like a lot at first glance, remember that we’ll be covering each topic one by one, gradually mastering the intricacies of NLP. The journey may be challenging, but step by step, we’ll build a solid foundation and become experts in the field of Natural Language Processing. Stay curious and keep learning!