Deep Dive into NLP Tokenisation and Embeddings

Natural Language Processing (NLP) has revolutionised the way machines understand human language. From chatbots to sentiment analysis, NLP applications are everywhere. At the heart of many NLP systems lie two critical processes: tokenisation and embeddings.

Understanding these foundational techniques is essential for anyone venturing into the field of NLP or aspiring to become a proficient data scientist. In this blog, we will take a comprehensive deep dive into NLP tokenisation and embeddings, exploring their types, applications, challenges, and advancements.

Whether you are a beginner or looking to deepen your knowledge, this blog will give you valuable insights into these crucial components of NLP. For those interested in advancing their skills, consider enrolling in a data science course in Mumbai to gain practical experience and in-depth learning about these topics.

What is Tokenisation in NLP?

Tokenisation is the first and one of the most critical steps in NLP. It involves breaking down text into smaller units called tokens. Tokens can be words, subwords, characters, or even sentences, depending on the tokenisation technique used. This process transforms raw text data into manageable pieces that can be analysed and processed by machine learning models.

Why is Tokenisation Important?

Text data in its raw form is unstructured and complicated for machines to interpret. Tokenisation provides structure and uniformity. Without tokenisation, algorithms would struggle to identify meaningful patterns or context in language.

For example, the sentence:

“Tokenisation helps machines understand language.”

can be tokenised into the following word tokens:

[“Tokenisation”, “helps”, “machines”, “understand”, “language”, “.”]

Each token serves as a fundamental building block for further analysis, such as part-of-speech tagging, parsing, or embedding generation.

Types of Tokenisation

1. Word Tokenisation

This is the most common form, where text is split into individual words based on spaces or punctuation. While simple, it can struggle with languages that do not use spaces (like Chinese or Japanese) and with compound words or contractions.

2. Subword Tokenisation

Subword tokenisation splits words into smaller units, which helps with out-of-vocabulary words and rare terms. Popular methods include Byte Pair Encoding (BPE) and WordPiece, widely used in models like GPT and BERT. For example, the word “unhappiness” might be tokenised into “un”, “happi”, and “ness”.

3. Character Tokenisation

Here, every character is treated as a token. This approach is helpful for languages with complex morphology or when dealing with noisy data, such as typos.

4. Sentence Tokenisation

Instead of words, this splits text into sentences. It is helpful for tasks like summarisation and translation where sentence-level context matters.

Challenges in Tokenisation

• Ambiguity: Words can have multiple meanings depending on context.
• Compound Words and Hyphenation: Deciding whether to split compound words or treat them as single tokens.
• Languages and Scripts: Tokenisation strategies must adapt to different languages, alphabets, and punctuation.
• Handling Contractions: For example, “don’t” can be split into “do” and “not” or treated as a single token.
• Tokenisation Consistency: Inconsistent tokenisation can affect model performance, especially in multilingual contexts.

What are Embeddings?

Once tokenised, the text tokens need to be converted into numerical representations so that machine learning algorithms can process them. This is where embeddings come in.

Embeddings are dense vector representations of tokens (words, subwords, or characters) in a continuous vector space. The idea is to capture semantic meaning and relationships between tokens such that similar words have similar vectors. This numerical form allows algorithms to perform mathematical operations and learn language patterns. If you’re eager to explore the world of NLP and build impactful projects, consider joining a data science course in Mumbai to get structured training and hands-on practice. With the rapid advancement in NLP technology, now is the perfect time to deepen your knowledge and stay ahead in the data-driven future.

Types of Embeddings

1. One-Hot Encoding

The simplest embedding, representing each token as a vector with all zeros except for a single one indicating its index in the vocabulary. While easy to understand, this approach is sparse and doesn’t capture semantic relationships.

2. Count Vectors and TF-IDF

These methods represent tokens based on their frequency in documents, which helps with basic document classification tasks. However, they don’t capture word order or semantic meaning.

3. Word2Vec

A breakthrough technique that learns embeddings by predicting context words around a target word (or vice versa). It captures semantic relationships efficiently, allowing analogies like “king” – “man” + “woman” ≈ “queen.”

4. GloVe (Global Vectors for Word Representation)

GloVe uses matrix factorisation on a word co-occurrence matrix, balancing local context and global statistics for embeddings.

5. Contextualised Embeddings (ELMo, BERT, GPT)

Unlike static embeddings (Word2Vec, GloVe), contextual embeddings produce different vectors for the same word depending on its context. This approach improves understanding of polysemy and syntax.

Why Are Embeddings Important?

Embeddings translate complex language concepts into a form that machines can understand and manipulate. They serve as the input to downstream NLP models for tasks such as sentiment analysis, machine translation, question answering, and more.

For example, in sentiment analysis, embeddings help the model understand that “happy” and “joyful” are similar, affecting how the model predicts sentiment.

The Relationship Between Tokenisation and Embeddings

Tokenisation and embeddings work hand in hand. The quality of tokenisation directly affects the quality of embeddings generated. Poor tokenisation can lead to fragmented or misrepresented words, causing embeddings to lose semantic accuracy.

Advances in subword tokenisation combined with contextual embeddings have enabled models to handle rare words, misspellings, and multiple languages more effectively.

Real-World Applications and Use Cases

• Chatbots and Virtual Assistants: Tokenisation breaks user queries into understandable parts, and embeddings help generate meaningful responses.
• Search Engines:Embeddings improve relevance by understanding query context and synonyms.
• Spam Detection: Models use embeddings to detect subtle patterns in email text.
• Sentiment Analysis: Understanding customer opinions on products or services.
• Machine Translation: Tokenisation segments input text, and embeddings help translate meaning across languages.

How to Master Tokenisation and Embeddings?

If you want to master these foundational NLP concepts and become proficient in building sophisticated language models, enrolling in a data scientist course can be a game-changer. These courses offer hands-on experience, covering tokenisation, embeddings, and state-of-the-art NLP architectures like transformers.

During the course, you’ll learn how to implement tokenisation methods using popular libraries such as NLTK, SpaCy, and Hugging Face Transformers. Additionally, you’ll explore embedding generation techniques and fine-tuning pre-trained models to suit specific tasks.

Emerging Trends in Tokenisation and Embeddings

• Multilingual Tokenisation: Models like mBERT support tokenisation across multiple languages, improving global NLP applications.
• Dynamic Tokenisation: Adaptive tokenisers that can optimise token granularity based on context.
• Sparse Embeddings: Techniques to reduce the size of embeddings while retaining their effectiveness, enhancing deployment efficiency.
• Integration with Knowledge Graphs: Combining embeddings with structured knowledge to improve reasoning capabilities.

Conclusion

Tokenisation and embeddings are the backbone of Natural Language Processing. Tokenisation transforms raw text into meaningful units, while embeddings translate these units into numerical forms that capture semantic relationships. Together, they enable machines to understand and generate human language effectively.

Whether you are beginning your journey in NLP or looking to specialise, understanding these concepts deeply is essential. Taking a data scientist course will equip you with the skills to implement and innovate in this exciting field. The journey from tokenising text to leveraging robust embeddings can open doors to numerous career opportunities in AI and data science.

Business Name: ExcelR- Data Science, Data Analytics, Business Analyst Course Training Mumbai
Address:  Unit no. 302, 03rd Floor, Ashok Premises, Old Nagardas Rd, Nicolas Wadi Rd, Mogra Village, GundavaliGaothan, Andheri E, Mumbai, Maharashtra 400069, Phone: 09108238354, Email: enquiry@excelr.com.