Part-of-speech (POS) tagging is a popular Natural Language Processing process which refers to categorizing words in a text (corpus) in correspondence with a particular part of speech, depending on the definition of the word and its context.
Part-of-speech tags describe the characteristic structure of lexical terms within a sentence or text, therefore, we can use them for making assumptions about semantics. Other applications of POS tagging include:
Named Entity Recognition
Co-reference Resolution
Speech Recognition
Taking the example, “Why not tell someone?”, imaging the sentence is truncated to “Why not tell … ” and we want to determine whether the following word in the sentence is a noun, verb, adverb, or some other part-of-speech.
Now, if you are familiar with English, you’d instantly identify the verb and assume that it is more likely the word is followed by a noun rather than another verb. Therefore, the idea as shown in this example is that the POS tag that is assigned to the next word is dependent on the POS tag of the previous word.
By associating numbers with each arrow direction, of which imply the likelihood of the next word given the current word, we can say there is a higher likelihood the next word in our sentence would be a noun since it has a higher likelihood than the next word being a verb if we are currently on a verb.
References:
https://towardsdatascience.com/part-of-speech-tagging-for-beginners-3a0754b2ebba
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