Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form—generally a written word form. The stem need not be identical to the root of the word; it is usually sufficient that related words reduce to the same stem, even if this stem is not a real word; it is generally enough that related terms reduce to the same stem, even if they do not all share the same root. For example, reduction of the words “fishing”, “fished”, and “fisher” to the stem “fish” would be considered stemming.
Moreover, it is a part of pre-processing text data for many NLP tasks such as part-of-speech tagging, named entity recognition, topic modeling, and document classification. It can increase the accuracy of these tasks by reducing the variation in word forms. For example, the words “run”, “ran”, and “running” would all be reduced to their stem “run”.
Advantages of Stemming in NLP
There are several advantages of using stemming in natural language processing:
- It can help reduce the size of the text data set.
- It can improve the accuracy of some NLP tasks by reducing the variation in word forms.
- It can make it easier to work with inflected languages.
Disadvantages of Stemming in NLP
There are also some disadvantages to using stemming:
- It can sometimes create non-existent words, which may be difficult for humans to interpret.
- It can reduce the accuracy of some NLP tasks by discarding information about word forms.
When to use stemming in NLP?
Whether or not to use stemming in natural language processing depends on the task at hand. In general, it is a good idea to use stemming if you are working with a large text data set and if you want to improve the accuracy of your NLP tasks. However, you should be aware of the potential disadvantages of using stemming before implementing it in your own text processing pipeline.
What is the difference between stemming and lemmatization?
Lemmatization is a related process where words are reduced to their base form. However, lemmatization is usually more sophisticated than stemming and takes into account the context of the word in addition to the word’s inflection. For example, the lemma of the word “better” is “good”, not “bett”. As a result, lemmatization can sometimes produce more accurate results than stemming. However, it is also usually more computationally expensive.