‘Text stemming in NLP’ is gradually becoming a buzzword in the data science learning programme world. But, what exactly is this? This blog will explore the introductory foundation of stemming text in natural language processing (NLP).

‘Stemming’- What’s that mean?

Stepping is a cutting edge technique for reducing the word length. Each complex word (in the case of computer science) contains the simplest root (stem) words along with respective prefixes and suffixes as the extended head and tails of the main word. Stemming indicates the manner of cutting the extended heads and tails to find out the root word.

As the name suggests, the concept of ‘stemming’ originates from the tree stem, i.e. the tree branches. Unlike cutting of the tree branches, curing of the word branching is referred to as stemming.

For instance, we have three words such as walking, sitting, eaten. The root words for each of these three worlds are ‘walk’, ‘sit’, and ‘eat’, respectively.

What is the importance of text stemming in NLP?

Human language is an unsolved problem that there are more than 6500 languages worldwide. The tons of data are getting generated every day as we speak, we text, we tweet, from voice to text on every social application, and to get the insights of this text data, we need technology such as NLP. If you know, there are two types of data: one is structured, and another one is unstructured data.

Structured data used for Machine learning models and unstructured data is used for Natural language processing. Unfortunately, only 21% of structured data is available, so now you can estimate how much NLP is required to handle unstructured data.

To get the insights of the dataset of unstructured data to take out the important information from it. The important technique to analyze text data is text mining. Text mining is the technique to extract useful information from unstructured data by identifying and exploring a large amount of text data. Or we can say that text mining is used to convert the unstructured data to the structured dataset.

Normalization, lemmatization, stemming, and tokenisation is NLP techniques to get out insights from the data.
While the above mentioned four techniques are an integral part of each other in terms of NLP, the majority of machine learning new bees resemble the two terms lemmatization and stemming. But in actuality, these two are not at all the same.

What does lemmatization mean? How does it differ from stemming?

From the definition perspective, we need to transform the word into its root structure to complete the lemmatisation process.

Still, it seems identical to stemming? So let me explain a bit more.

In actuality, stemming and lemmatization are quite different. Stemming is less concerned about context and so converts the words to their root fabrication anyway.
On the other hand, lemmatization is more concerned with contextual accuracy and converting words to their root structure without changing their contextual inclination.

Let me explain with a simple example of the differentiated application of stemming and lemmatization in NLP. We all are well aware of the different search engines. Now, when you type something in the search panel of a particular search engine and hit the enter button, you get lots of relevant results. For example, suppose your search keywords were ‘COVID-19 Vaccination’. Now from the contextual meaning, the search results will contain all of the outputs that the search engine will find relevant to the stem word of ‘Vaccine’.

But if there is only stemming, then your search results will not be limited to the COVID-19 vaccine. Rather it may include other vaccines related information too. Here comes the importance as well as the difference of lemmatization. The latter process will consider the contextual importance of the vaccine in terms of COVID-19 and will provide the output (research result) accordingly.

The same thing applies when it comes to the application of both stemming and lemmatization in chatbots. While stemming breaks the words associated with the customer quarries into their stem and sub-branches, lemmatization works on maintaining the actual contexts of the words. Based on such language analysis, the concerned chatbot system reverts back with the resolution.

Apart from the above difference, It’s also vital to note another substantial difference between a lemma, which is the root structure of all its inflectional patterns, and a stem, which is not like that.
Concerning the above difference, two situations can originate. In one situation, depending on the particular inflectional pattern, the stem can be the same for all the lemmas.

In another situation, an item can have multiple forms that use the same lemma.
Such circumstances usually come when working with multiple languages. For example, it may be possible that the stem of two different words in English corresponds to the same lemma and vice versa, while it’s converted to the concerned regional language.

Now we will see how text stemming works?

As already mentioned, stemming is the process of reducing inflexion in words to their “root” forms, such as mapping a group of words to the same stem. Stem words mean the suffix and prefix that have been added to the root word.

In computer science, we need this process to produce grammatical variants of root words. A stemming is provided by the NLP algorithms that are stemming algorithms or stemmers. The stemming algorithm removes the stem from the word. For example, ‘walking’, ‘walks’, ‘walked ‘are made from the root word ‘Walk’. So here, the stemmer removes ing, s, ed from the above words to take out the meaning that the sentence is about walking in somewhere or on something. The words are nothing but different tenses forms of verbs.

Below is an example of stem ‘Consult.’ see how addition of different suffixes generated longer form of the same stem.

This is the general idea to reduce the different forms of the word to their root word.

Words that are derived from one another can be mapped to a base word or symbol, especially if they have the same meaning.

What are the most common types of error associated with text stemming in text mining or NLP?

We can not be sure that it will give us a 100% result, so we have two types of error in stemming: over stemming and under stemming.

What is Over stemming error?

This kind of error occurs when there are too many words cut out. It may be possible that the segmentation of the long-form word may give birth to two such stems that are identical but may actually differ in contextual meaning. These could be known as nonsensical items, where the meaning of the word has lost, or it can not be able to distinguish between two stems or resolve the same stem where they should differ from each other.

For example, take out the four words university, universities, universal, and universe. A stemmer that resolves these four stems to “Univers” is over-stemming. It should be the universe stemmer that stemmed together, and university, universities stemmed together they all four are not fit for the single stem.

What is Under stemming error?

Under-stemming is the opposite of stemming. It comes from when we have different words that actually are forms of one another. It would be nice for them to all resolve to the same stem, but unfortunately, they do not.

This can be seen if we have a stemming algorithm that stems from the words data and datum to “dat” and “datu.” And you might be thinking, well, just resolve these both to “dat.” However, then what do we do with the date? And is there a good general rule? So there under stemming occurs.

Where to learn more about stemming for data science?

In case you want to learn text stemming from scratch, you can join the Learnbay data science course.

Learnbay provides industry accredited data science courses in Bangalore. We offer data science and AI courses to both working professionals and freshers. You’ll avail end-to-end learning guidance about important measures and techniques of text analysis using NLP. You can choose your live project on applying text stemming as a solution to any of your domain-related issues. We deploy our students to different product-based MNCs or startups for a real-time industrial project. So after the completion of the course, you’ll get a highly creditable project experience certificate issued by the concerned company.

We understand the conjugation of technology in the field of Data science; hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies with lucrative data science salaries in India. By choosing Learnbay, you will reach the most aspiring job of the present and future. All of our courses and modules are certified by IBM.

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