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Text Stemming In NLP

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Human language is an unsolved problem that there are more than 6500 languages worldwide. The tons of data are generated every day as we speak, we text, we tweet, from voice to text on every social application and to get the insights of these text data we need technology as Text Stemming In NLP. If you know there are two types of data are there one is structured and unstructured data. Structured data used for Machine Learning models and unstructured data is used for Natural language processing. There are only 21% of structured data is available, so now you can estimate how much Text Stemming In 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 the text data is text mining. Text mining is the technique to extract useful information from the 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, tokenization is the technique in NLP to get out the insights from the data.

Now we will see how text it works?

Stemming is the process of reducing inflection 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 added to the root word. It is the process to produce grammatically 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, eats, eating, eatery, they are made from the root word “eat“. so here the stemmer removes s, ing, very from the above words to take out meaning that the sentence is about eating something. The words are nothing but different tenses forms of verbs.

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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.

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

Over stemming occurs when there are too many words have cut out.

This could be known as non-sensical 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” that 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.

Under stemming: 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.

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