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Preposition (P)- at, on, in, from, with, near, between, about, under.
Adverb(ADV)- slowly, quietly, very, always, never, too, well, tomorrow. Adjective(ADJ)- big, happy, green, young, fun, crazy, three. Verb (V)- go, speak, run, eat, play, live, walk, have, like, are, is. Noun (N)- Daniel, London, table, dog, teacher, pen, city, happiness, hope. Let’s examine the most used tags with examples. The tag in case of is a part-of-speech tag, and signifies whether the word is a noun, adjective, verb, and so on. It is a process of converting a sentence to forms - list of words, list of tuples (where each tuple is having a form (word, tag)). Snowball Stemmer (Algorithm details are in this link.). Porter Stemmer (Algorithm details are in this link.). We’ll examine the stemming example with two different algorithms. Understemming occurs when two words are stemmed from the same root that is not of different stems. In such cases, the meaning of the word may be distorted or have no meaning. Overstemming occurs when words are over-truncated. This reveals inconsistencies regarding stemming. Because Stemming works rule-based, it cuts the suffixes in words according to a certain rule. When you are breaking down words with stemming, you can sometimes see that finding roots is erroneous and absurd. A word stem need not be the same root as a dictionary-based morphological root, it just is an equal to or smaller form of the word. With stemming, words are reduced to their word stems. Stemming is definitely the simpler of the two approaches. Let’s examine a definition made about this. 
Stemming is the process of finding the root of words.
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