Day 4: Stemming and Lemmatization
Stemming and lemmatization are two popular techniques for text pre-processing in natural language processing (NLP) tasks. In this article, we will discuss what stemming and lemmatization are and provide examples to illustrate their application.
Stemming is the process of reducing a word to its root or stem form. For example, the stem of the word “running” is “run”. Stemming algorithms are based on rules and heuristics that remove affixes and suffixes to generate the stem of the word. Stemming is a relatively simple and fast process that can be applied to large amounts of text data.
Let’s see an example of Stemming in Python:
However, stemming has some limitations. For example, it can generate stems that are not actual words, which can affect the accuracy of NLP tasks such as text classification and sentiment analysis. Therefore, lemmatization is often preferred over stemming for more accurate and precise results.
Lemmatization is the process of reducing a word to its base or dictionary form, which is called the lemma. Unlike stemming, lemmatization takes into consideration the context of the word and its grammar, which makes it more accurate. Lemmatization requires access to a dictionary or knowledge base that maps words to their lemma forms.
Lemmatization produces more meaningful and accurate results than stemming, but it can be slower and more computationally expensive, especially when dealing with large amounts of text data.
In conclusion, stemming and lemmatization are important techniques for pre-processing text data in NLP tasks. Stemming is a simple and fast process that can be applied to large amounts of text data, but it may not produce actual words. Lemmatization, on the other hand, is a more accurate and precise process that takes into consideration the context of the word and its grammar, but it may be slower and more computationally expensive. The choice of technique depends on the specific needs of the NLP task at hand.