Difference between tokenization and lemmatization in NLP
It is often easy to detect people's emotions based on the inflections in their tone. But, as most of us have experienced while texting, our emotions don't fully come across unless we correctly express them. Just as this becomes an obstacle for humans, who can analyze each other's tone in real-life, computer software cannot understand the context and sentiment of any statement made by the users. Using natural language processing (NLP), many of the problems faced by computers can be overcome by enabling computers to understand and process human language.
What is NLP?
Natural language processing (NLP) is a field of artificial intelligence and computational linguistics that focuses on the interaction between computers and human language. The primary goal of NLP is to bridge the gap between human language and machine understanding. This is achieved through various techniques and methods to process, analyze, and generate natural language text or speech. NLP encompasses a wide range of tasks and applications, including:
Text classification and sentiment analysis
Information extraction
Machine translation
Text summarization
Speech recognition and speech synthesis
Chatbots and virtual assistants
Language generation
In these applications, the fundamental steps are pretty similar, on which tokenization and lemmatization will be focused. Both of these methods are used in the preprocessing phase of NLP tasks.
What is tokenization?
Tokenization is a crucial step in NLP tasks where text is divided into smaller units called tokens. These tokens can range from individual characters to complete sentences, but they are often individual words or subwords. The choice of token size has a notable impact on the performance of an NLP model and the computational resources it requires. This process of tokenizing any document helps in the preprocessing part of any NLP model training. Moreover, tokens also serve as features in various NLP models and algorithms, providing the basis for representing text data in numerical form.
Some of the most commonly used tools for tokenization are:
NLTK
TextBlob
SpaCy
Gensim
Keras
You can read in detail about tokenization using Gensim in this Answer.
What is lemmatization?
In the field of NLP, lemmatization is the process of reducing words to their base form, known as the lemma. It helps group similar words by considering their meaning and grammatical structure. This helps ensure that words with similar meanings are treated the same. By lemmatizing words, NLP models can achieve better accuracy and consistency in language analysis tasks. Lemmatization is vital in tasks like information retrieval and text classification for better understanding and analysis of words.
The most commonly used libraries for lemmatization are
NLTK
spaCy
Scikit-Learn
Stanford CoreNLP
Gensim
You can explore more on lemmatization in this Answer.
With all this in mind, let's now turn our attention to the difference between tokenization and lemmatization, as these are both essential methods used in the preprocessing phase of NLP tasks.
Difference between tokenization and lemmatization
Tokenization and lemmatization are essential for text preprocessing, where raw text is prepared for further analysis. By dividing the text into tokens and lemmatizing words, the text becomes more structured, manageable, and suitable for subsequent NLP tasks. Furthermore, tokens also serve as features enhanced by lemmatization by reducing the dimensionality and capturing the essential semantics of words. It also helps reduce vocabulary size by grouping inflected forms of words. It improves vocabulary coverage by representing words in their base forms, allowing the NLP model to generalize better and handle different word forms more effectively. All these applications show the significance of both methods in NLP. However, let’s see the difference between them.
Differences between tokenization and lemmatization
Tokenization | Lemmatization |
Involves breaking down text into individual tokens | Involves reducing words to their base or canonical form |
Tokens are the basic units of text (words or subwords) depending on the chosen granularity | Considers the morphological and grammatical properties of words to determine their base form |
Helps in segmenting text and dividing it into meaningful units | Helps to group derived forms of a word, treating them as the same word and meaning. |
Often an initial step in NLP preprocessing | Helps in normalizing words, reducing vocabulary size, and improving language analysis accuracy |
Let's take a short quiz for better understanding.
Assessment
What are the main differences between tokenization and lemmatization, and how do they work?
Tokenization and lemmatization are the same processes, where both split the text into individual words and convert them to their base forms for analysis.
Tokenization is the process of converting text into individual words or tokens, while lemmatization is the process of converting words to their base or root forms.
Tokenization is used for converting words to their base forms, while lemmatization is used for splitting text into individual words.
Tokenization is a more complex process than lemmatization, involving the analysis of grammatical structures to understand word meanings.
Summary
In summary, tokenization focuses on dividing the text into smaller units (tokens), while lemmatization focuses on reducing words to their base form (lemma). Tokenization serves as the basis for text segmentation, whereas lemmatization aids in standardizing words and improving language understanding and analysis. Both techniques are commonly used in NLP preprocessing and are often applied sequentially to prepare text data for further analysis or modeling.
